{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook to compare modelled (MERRA-2, SNOWPACK, RACMO2, and MAR) vs observed SMB from IceBridge snow accumulation radar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import cartopy.crs as ccrs\n",
    "import xarray as xr\n",
    "from osgeo import osr\n",
    "import pyproj\n",
    "from pyproj import Geod\n",
    "from pyproj import Proj, transform\n",
    "from fiona.crs import from_epsg\n",
    "from calendar import monthrange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load observed SMB from Dattler et. al., 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_lat, obs_lon, obs_accumulation, relative_accumulation, relative_accumulation_error = \\\n",
    "    np.loadtxt(\"/pl/active/nasa_smb/Data/Accumulation_Data_Product.csv\", skiprows=1, delimiter=',', unpack=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform obs lat/lon into epsg 3031"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source and target EPSG\n",
    "src = osr.SpatialReference()\n",
    "tgt = osr.SpatialReference()\n",
    "src.ImportFromEPSG(4326) # WGS-84\n",
    "tgt.ImportFromEPSG(3031) # South Polar Stereo\n",
    "\n",
    "# Define transformation\n",
    "transform = osr.CoordinateTransformation(src, tgt)\n",
    "\n",
    "# Initialize arrays\n",
    "obs_X = np.zeros(len(obs_lon)); obs_X[:] = np.nan\n",
    "obs_Y = np.zeros(len(obs_lon)); obs_Y[:] = np.nan\n",
    "\n",
    "# Perform transformation\n",
    "for j in range(0, len(obs_X)):\n",
    "    coords = transform.TransformPoint(obs_lat[j], obs_lon[j]) # Lat, Lon\n",
    "    obs_X[j], obs_Y[j] = coords[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load SNOWPACK SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SNOWPACK SMB\n",
    "nc_path = \"../output/grids/a3d_grids.nc\"\n",
    "ds = xr.open_dataset(nc_path)\n",
    "\n",
    "# Trim grids\n",
    "ds = ds.isel(easting=slice(5, -5))\n",
    "ds = ds.isel(northing=slice(5, -5))\n",
    "\n",
    "# # SNOWPACK topography \n",
    "# dem = np.flipud(np.loadtxt(\"../input/surface-grids/dem.asc\", skiprows=6))\n",
    "# dem = xr.DataArray(dem, coords=[ds['northing'], ds['easting']], dims=['northing', 'easting'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine SNOWPACK domain corners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_left = ds['easting'].min()\n",
    "domain_right = ds['easting'].max()\n",
    "domain_bottom = ds['northing'].min()\n",
    "domain_top = ds['northing'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot observed SMB (blue circles) and model domain (red rectangle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/projects/erke2265/miniconda/envs/alpine3d/lib/python3.6/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b244b265588>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coastline Data\n",
    "df = gpd.read_file(\"/pl/active/nasa_smb/Data/ADD_Coastline_low_res_polygon.shp\")\n",
    "crs_epsg = ccrs.SouthPolarStereo()\n",
    "df_epsg = df.to_crs(epsg='3031')\n",
    "\n",
    "# Map\n",
    "# Generate figure \n",
    "fig, axs = plt.subplots(1, 1, subplot_kw={'projection': crs_epsg},\n",
    "                        figsize=(10, 10))\n",
    "\n",
    "# Plot coastlines\n",
    "axs.set_extent((-180, 180, -90, -65), ccrs.PlateCarree())\n",
    "axs.add_geometries(df_epsg['geometry'], crs=crs_epsg,\n",
    "                      facecolor='none', edgecolor='black')\n",
    "\n",
    "# Plot obs locations\n",
    "plt.scatter(obs_X, obs_Y)\n",
    "\n",
    "# Plot model domain\n",
    "plt.scatter(domain_left, domain_bottom, c='r')\n",
    "plt.scatter(domain_left, domain_top, c='r')\n",
    "plt.scatter(domain_right, domain_bottom, c='r')\n",
    "plt.scatter(domain_right, domain_top, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine indices of observations which are in SNOWPACK model domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get indices of observations inside of the model domain\n",
    "def get_domain_obs(obsX, obsY, obs_accum, domain_left, domain_right, domain_bottom, domain_top, obs_lat, obs_lon):\n",
    "    '''\n",
    "    Find obs whose X and Y are:\n",
    "        X: greater than domain_left and less than domain_right\n",
    "        Y: greater than domain_bottom and less than domain_top\n",
    "    ''' \n",
    "    filter_func = np.vectorize(lambda obsX, obsY: obsX <= domain_right and obsX >= domain_left and obsY >= domain_bottom and obsY <= domain_top)\n",
    "    indices = filter_func(obsX, obsY)\n",
    "    obs_accum_filter = obs_accum[indices]\n",
    "    obsX_filter = obsX[indices]\n",
    "    obsY_filter = obsY[indices]\n",
    "    obs_lat_filter = obs_lat[indices]\n",
    "    obs_lon_filter = obs_lon[indices]\n",
    "    \n",
    "    return obsX_filter, obsY_filter, obs_accum_filter, obs_lat_filter, obs_lon_filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve observed SMB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Retrieve observations\n",
    "obs_X, obs_Y, obs_accumulation, obs_lat, obs_lon = get_domain_obs(obs_X, obs_Y, obs_accumulation, domain_left, domain_right, domain_bottom, domain_top, obs_lat, obs_lon)\n",
    "\n",
    "# Relative observations\n",
    "# obs_X, obs_Y, obs_accumulation, obs_lat, obs_lon = get_domain_obs(obs_X, obs_Y, relative_accumulation, domain_left, domain_right, domain_bottom, domain_top, obs_lat, obs_lon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve SNOWPACK SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve SNOWPACK at observations\n",
    "SNOWPACK_SMB = ds['swe'].isel(time=-1) - ds['swe'].isel(time=0)\n",
    "SNOWPACK_SMB = SNOWPACK_SMB.sel(northing=obs_Y, easting=obs_X, method='nearest')\n",
    "SNOWPACK_SMB = SNOWPACK_SMB.values.diagonal()\n",
    "SNOWPACK_SMB = SNOWPACK_SMB * 1000\n",
    "\n",
    "# # Retrieve elevation at observations\n",
    "# dem_obs = dem.sel(northing=obs_Y, easting=obs_X, method='nearest')\n",
    "# dem_obs = dem_obs.values.diagonal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieve RACMO2 SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/projects/erke2265/miniconda/envs/alpine3d/lib/python3.6/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "/projects/erke2265/miniconda/envs/alpine3d/lib/python3.6/site-packages/ipykernel_launcher.py:11: DeprecationWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import pyproj\n",
    "from pyproj import Proj, transform\n",
    "from fiona.crs import from_epsg\n",
    "\n",
    "# Define source epsg and target projection. The later is obtained from ncdump. \n",
    "src_epsg = 3031 # Antarctic Stereo\n",
    "tgt_proj = \"-m 57.295779506 +proj=ob_tran +o_proj=latlon +o_lat_p=-167.0 +lon_0=53.0\"\n",
    "\n",
    "# Convert array of lat/lon in source epsg to the rotated grid\n",
    "rlon_obs, rlat_obs = transform(Proj(from_epsg(src_epsg)), Proj(tgt_proj), obs_X, obs_Y)\n",
    "\n",
    "# Now retrieve RACMO2 data at grid cells closest to lat/lon array\n",
    "# R2 = xr.open_dataset(\"smb_avg.nc\")\n",
    "R2 = xr.open_dataset(\"year_cum_smb_ASE055_1979-2015.nc\")\n",
    "R2_SMB = R2['smb']\n",
    "\n",
    "lat_indices = np.zeros(len(rlat_obs)); lat_indices[:] = np.nan\n",
    "lon_indices = np.zeros(len(rlat_obs)); lon_indices[:] = np.nan\n",
    "R2_obs = np.zeros(len(rlat_obs)); R2_obs[:] = np.nan\n",
    "\n",
    "for j in range(0, len(rlat_obs)):\n",
    "    lat_diff = np.abs(R2['rlat'] - rlat_obs[j])\n",
    "    lat_indices[j] = np.argmin(lat_diff)\n",
    "    lon_diff = np.abs(R2['rlon'] - rlon_obs[j])\n",
    "    lon_indices[j] = np.argmin(lon_diff)\n",
    "    R2_obs[j] = R2_SMB[1,0,int(lat_indices[j]), int(lon_indices[j])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve MERRA-2 SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate seconds in each month from 1980 - 2019\n",
    "seconds = np.zeros(480); seconds[:] = np.nan\n",
    "count = -1\n",
    "for year in range(1980, 2019 + 1):\n",
    "    for month in range(1, 12 + 1):\n",
    "        count = count + 1\n",
    "        tmp = monthrange(year, month)\n",
    "        seconds[count] = float(tmp[1]) * 24 * 60 * 60\n",
    "\n",
    "# Path\n",
    "data_path = \"/projects/erke2265/SOTC_AIS_SMB/nc_files/\"\n",
    "\n",
    "# Precipitation\n",
    "sn = xr.open_dataset(data_path + \"PRECSN_monthly_1980.nc\")\n",
    "ls = xr.open_dataset(data_path + \"PRECLS_monthly_1980.nc\")\n",
    "cu = xr.open_dataset(data_path + \"PRECCU_monthly_1980.nc\")\n",
    "\n",
    "# Evaporation \n",
    "evap = xr.open_dataset(data_path + \"EVAP_monthly_1980.nc\")\n",
    "\n",
    "# SMB\n",
    "M2_smb = sn['PRECSN'] + ls['PRECLS'] + cu['PRECCU'] - evap['EVAP']\n",
    "\n",
    "# Convert smb from per second to accumulated\n",
    "for j in range(0, 12):\n",
    "    M2_smb[j,:,:] = M2_smb[j,:,:] * seconds[j]\n",
    "    \n",
    "# Now sum over time\n",
    "M2_smb = M2_smb.sum(dim='time')\n",
    "\n",
    "# Now get MERRA-2 at the observaions locations\n",
    "M2_obs = M2_smb.sel(lat=obs_lat, lon=obs_lon, method='nearest')\n",
    "M2_obs = M2_obs.values.diagonal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve MAR SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAR_path = \"/pl/active/nasa_smb/MAR/MARv3.6.4-Antarctica/MERRA2/\"\n",
    "MAR_smb = xr.open_dataset(MAR_path + \"MAR-MERRA2_smb_1980-2015_annual.nc4\")\n",
    "MAR_obs = MAR_smb['smb'][0,0,:,:].sel(x=obs_X/1000, y=obs_Y/1000, method='nearest')\n",
    "MAR_obs = MAR_obs.values.diagonal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Horizontal ransect of SNOWPACK vs obserced SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Observations\n",
      "    435.5984708202768\n",
      "\n",
      "Mean Alpine-3D\n",
      "    316.8061218261719\n",
      "    Alpine-3D Factor = 1\n",
      "\n",
      "Mean MERRA-2\n",
      "    323.63861083984375\n",
      "    MERRA-2 Factor = 1\n",
      "\n",
      "Mean RACMO2\n",
      "    279.7322029824136\n",
      "    RACMO2 Factor = 1\n",
      "\n",
      "Mean MAR\n",
      "    319.6668395996094\n",
      "    MAR Factor = 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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v/A4jW5R0EhEREREREZGwt23bNu68805q1qxJ8eLFqVGjBnfccQdbt27N79BCYv78+ZgZe/bsCdr/8ssvM2nSpHyKKmeUdBIRERERERGRsLZx40batWvHqlWrmDhxIuvXr2fSpEnExsbSvn174uPj8y2248eP5+r1y5YtS7ly5XL1HrlFSScRERERERERCWv33HMPZ511Fl9//TXdu3endu3adOvWja+//pqzzjqLe+65J7nvyZMn+cc//kH58uUpX748Dz/8MKdOnUo+/tFHH9GqVStKlixJhQoVuOiii9i5c2fy8c8++4zzzjuPiIgI6tWrx/Dhw4MSS3Xr1mXkyJEMHDiQcuXK0a9fPzp37syDDz4YFPOBAwcoWbIkH3/8MQCTJk2iffv2REVFUaVKFW688Ua2bdsGQHx8PN26dQOgcuXKmBkDBgwAUg+vO3bsGPfddx8NGjQgIiKCTp068b///S/5eFLF1Ny5c+nYsSORkZG0a9eOpUuXJvf5448/iI6OpkqVKkRERFC/fn1eeumlbH8+6VHSSURERERERORMZZZ/j0z6/fffmT17Nvfccw+RkZFBxyIjI7n77rv54osv2LdvHwCTJ0/m1KlTLFiwgP/+97+MHTs2OaGyY8cObrrpJm699Vbi4uL47rvviI6OTr7enDlz6NevH0OGDCE2Npbx48czbdo0hg0bFnTfF154gaZNm7J48WL++c9/0r9/fz744IOg5Nb06dMpWbIkV1xxBeBVRI0aNYrly5czc+ZM9uzZw8033wxArVq1mD59OgCxsbEkJCTw8ssvp/l+PPLII0yZMoXXXnuNX375hZYtW9KzZ08SEhKC+g0dOpRnnnmGpUuXUrFiRfr164dzDoDHH3+clStXMnPmTFavXs348eOpUaNGpj+TzCoa8iuKiIiIiIiIiITIunXrcM7RrFmzNI+fc845OOdYt24dANWqVeOVV17BzGjatClr167lhRde4IEHHmD79u2cOHGCG264gTp16gDQokWL5Gs9/fTTPPzww9x2220ANGjQgGeffZb+/fvz/PPPY75k2UUXXcQjjzySfF6FChW4//77mTdvHt27dwe85NeNN95I8eLFARg4cGBy//r16/PGG2/QrFkztm7dSs2aNalQoQIAVapUoVKlSmm+1sOHD/PGG28QExNDz549iYqK4s033+Sbb77htddeY8yYMcl9R48enVw9NWLECC644AK2bdtGzZo12bRpE23atKFDhw6AV72VG1TpJCIiIiIiIiJhz9Kpjkqq3kk63qlTp6C+nTt3Ztu2bRw4cIDWrVvTo0cPWrRowfXXX88bb7zB7t27k/suWbKEp59+mtKlSyc/+vbty+HDh9mxY0dyv3bt2gXFULFiRS677DImT54MQEJCAvPmzaN///7JfZYuXcrVV19NnTp1iIqKSr7G5s2bM/0e/Pbbb5w4cYIuXbok7ytSpAidO3fm119/DerbqlWr5Hb16tUB2LVrFwCDBw9m6tSptG7dmoceeohvv/020zFkhZJOIiIiIiIiImcq5/LvkUmNGjXCzIiNjU3zeFxcHGZGgwYNTnutIkWK8OWXX/Lll1/SqlUrxo0bR6NGjVi+fDkAp06d4sknn2TZsmXJjxUrVrBu3ToqV66cfJ1SpUqlunb//v2ZPn06R48e5f3336dWrVpccMEFgFehdNlllxEZGcm7777Lzz//zOzZs4GsTUSeMsEWKOW+YsWKpTqWNPzv8ssvZ9OmTTz00EPs2bOHK664Irm6K5SUdBIRERERERGRsFWhQgUuu+wyXn/9dY4cORJ07MiRI7z22mtcfvnlycPTFi5cmJycAfjpp5+oXr06ZcqUAbwETOfOnXnyySf5+eefqV69OlOmTAGgbdu2rF69moYNG6Z6FC2a8QxFV199NQAzZ85k8uTJ9OvXLznZs3r1avbs2cM///lPLrzwQpo2bZpcdZQkaRheYmJiuvdo2LAhxYsXD5o4PDExkQULFnDOOedkGF9KlSpVIjo6mgkTJjBu3DgmTpzIsWPHsnSN01HSSURERERERETC2quvvsrJkyfp0aMH33zzDVu2bGH+/PlccsklOOd49dVXk/tu376d++67jzVr1jBt2jSef/557r//fsBLQI0ZM4aff/6ZzZs3M2PGDLZs2ZKcsBkxYgTvvfceI0aMYNWqVaxevZpp06YFzd+UnoiICK677jrGjBnD0qVLg4bW1a5dmxIlSvDqq6+yYcMGZs2axRNPPBF0fp06dTAzZs2axe7duzl06FCqe5QqVYrBgwfz2GOPMWfOHOLi4hg8eDA7d+7k7rvvzvT7OWLECD755BPWrVtHXFwcH330EfXr16dEiRKZvkZmKOkkIiIiIiIiImGtQYMGLF68mObNmxMdHU39+vXp27cvzZo14+eff6ZevXrJffv160diYiIdO3bkjjvuYNCgQclJp7Jly/LDDz/Qq1cvGjVqxIMPPsgTTzyRnCC67LLLmDVrFvPmzaNDhw506NCBZ555htq1a2cqzujoaJYvX07btm2DJj6vXLkyEydO5JNPPuGcc85h1KhRvPDCC0Hn1qhRg1GjRjF8+HDOPvtshgwZkuY9nn32WXr37s3dd9/Nueeey4oVK5g9ezbVqlXL9PtZokQJhg8fTuvWrenSpQsHDx7ks88+y/T5mWUuC+MoC7p27dq5xYsX53cYOTZ//ny6du2a32GIFDj67ohknb43Ilmn741I1ul7k3fi4uLSXQVOCpaDBw8SFRWVZ/fL6GfHzJY459ql3K9KJxERERERERERCTklnUREREREREREJOSUdBIRERERERERkZBT0klEREREREREREJOSScREREREREREQk5JZ1ERERERERERCTk8jTpZGbjzWyXma0K2FfBzL4ys3W+5/IBx4aa2XozW2NmlwXsP8/MVvqOvWJmlpevQ0REzkDHjsH+/fkdhYiIiIhIgZHXlU4TgJ4p9j0GzHXONQLm+rYxs3OAm4DmvnNeN7MivnPeAO4EGvkeKa8pIiISOuvXQ61aUL06fPghvP8+vPgirF0L33wDEyZ4SSkREREREUlWNC9v5pz7zszqpth9NdDV154IzAce9e3/wDl3DNhoZuuBDmYWD5Rxzi0AMLN3gGuAL3I5fBEROVONHg27d3vt3r39+x94wN/evh2GDcvbuEREREREwlieJp3ScbZzLgHAOZdgZlV8+2sAPwX02+rbd8LXTrk/TWZ2J15VFGeffTbz588PXeT55NChQ4XidYjkNX13JDuK79lD50mTON047oNvv82S88/Pk5jykr43Ilmn741I1ul7k3fKli3LwYMH8zuMkCtTpgzvvPMO11xzTab6b9q0iZYtWzJ//nzatm2by9HljsTExDz9LI8ePZrl72k4JJ3Sk9a/710G+9PknBsLjAVo166d69q1a0iCy0/z58+nMLwOkbym745ky+uvw6lTwfvMwAX/rycqPp6uF13kHStE9L0RyTp9b0SyTt+bvBMXF0dUVFR+h5Etv/zyC+3ataNTp0788MMPqY6XLFky06+tWbNmJCQkUKlSJYoWzd3UyBNPPMGHH37Ili1bKF68OG3btmX06NGcH/AHy7p167Jp0yYASpQoQeXKlWnXrh0DBw7kyiuvTPO6Bw8ezNPPMiIigjZt2mTpnHBYvW6nmVUD8D3v8u3fCtQK6FcT2O7bXzON/SIiIqG3fLm//a9/QXw8HDoER44ED687eRK2bMnz8ERERETOFG+99RZ33303q1atIi4uLkfXKlKkCFWrVs31hBNAkyZNeO2111i5ciX/+9//qFevHj179mTnzp1B/UaMGEFCQgJr167lgw8+oG7dulx77bX8/e9/z/UYc0s4JJ1mALf62rcCnwbsv8nMSphZPbwJwxf5huIdNLNOvlXrbgk4R0REJLRWrfK327aFOnUgMhJKloT/+z/4y1/8x1euzPv4RERERM4Af/75J++99x533HEHN9xwA+PGjUu3b3x8PGbGe++9xwUXXEBERARNmzblyy+/TNVn8eLFgFdtZ2bMnTuXjh07EhkZSbt27Vi6dGnQtX/88UcuuugiIiMjqVGjBoMHD+bAgQMZxt6/f3+6d+9O/fr1ad68OS+88AIHDx5k2bJlQf2ioqKoWrUqtWvXpkuXLrz44ou8/vrrvPrqq8ybNy+rb1lYyNOkk5m9DywAmpjZVjMbBDwDXGJm64BLfNs452KBqcCvwGzgHudcou9Sg4EYYD3wG5pEXEREcoNzwUmnFi1S92nZ0t/u1Qu2bcv9uERERERCxCz/Hlkxbdo06tSpQ6tWrYiOjuadd97hxIkTGZ7zyCOPcO+997Js2TIuueQSrr76arad5t9qQ4cO5ZlnnmHp0qVUrFiRfv364XzTKqxcuZJLL72Uq666iuXLl/PRRx+xbNkyBg4cmOnXcfz4ccaOHUuZMmU499xzT9t/0KBBlC9fnunTp2f6HuEkr1evuzmdQ93T6f808HQa+xcDafzLX0REJIS2bIGkv1xVqADVqqXukzIRNXIkvPVWrocmIiIiciaJiYkhOjoaILnSaMaMGVx//fXpnjN48GB6+1Yefvnll5kzZw5vvPEGY8aMSfec0aNH061bN8Ab7nbBBRewbds2atasyfPPP0+fPn148MEHk/u/8cYbtGnThl27dlGlSpX0LsvMmTO56aabOHLkCNWqVeOrr77i7LPPPu3rLlKkCI0bN2bDhg2n7RuOwmF4nYiISHgKHC7XokXaf5K79trg7YULczcmERERkTPM+vXr+eGHH+jbty8AZka/fv2IiYnJ8LzOnTsnt8866yw6duzIr7/+muE5rVq1Sm5Xr14dgF27vKmnlyxZwqRJkyhdunTyo0uXLgD89ttvTJ48OejY999/n3ytbt26sWzZMn788Ud69uxJ7969SUhIyNTrd85hBXSxmnBevU5ERCR/BZYxB/wDJEjVqt7k4nXrettr10JiIhQpktvRiYiIiOSYS3ct+PARExNDYmIitWvXTt6XNORty5Yt1KpVK71Ts6xYsWLJ7aREzynfSsanTp3i9ttv5/777091Xo0aNWjRogUdO3YM2pekVKlSNGzYkIYNG9KpUycaNWpETEwMTzzxRIbxJCYmsnbtWjp06JCj15VflHQSKcicK3TLs4uEBefgxRfh7bf9+3x/WUtTnTpe8mnHDjh2zEtCNWiQ62GKiIiIFHYnT55k4sSJ/Otf/6JXr15Bx6Kjo3n77bcZMWJEmuf+9NNPXHzxxYCXpFq0aBE33HBDtmNp27YtsbGxNGzYMN0+UVFRmbrWqVOnOHbs2Gn7xcTEsH///hzFnZ80vE6kIJo8GZo29SorVqzI72hECp+ZMyFgrD6dO3uPjDRt6m/ncAlfEREREfHMmjWLPXv2cMcdd9CiRYugx0033cT48eOTK5FSeuONN5g2bRpr1qzhvvvuY9OmTQwePDjbsTz66KMsWrSIv/3tb/zyyy+sX7+emTNnctddd6V7zoEDB3j88cdZuHAhmzdvZsmSJQwcOJCtW7cmzzeV5ODBg+zYsYMtW7bw448/cv/993PPPfcwZMgQLrroomzHnZ+UdBIpKH7/HS6+2Kts6t8f1qyBzZvhP//J78hECp8ffgjefjrVmhapNWvmbyvpJCIiIhIS48aNo1u3blSsWDHVsRtvvJFNmzbx9ddfp3nuM888wwsvvEDr1q2ZPXs2H3/8MTVr1sx2LK1ateK7774jPj6eiy66iNatWzN06NAMJwQvWrQosbGxXHvttTRq1Igrr7ySvXv38t133wXNHwXw1FNPUa1aNRo2bEjv3r3ZuHEjH330Ef8pwL/zaXidSEHx9tswb17q/YETHYtIaKxZ42+/8gr4VjDJkJJOIiIiIiE3Y8aMdI/Vr18/eW4nl8bkVE2aNOHHH39M89y6desGndO1a9dU10jZB6Bdu3bMnj070/FHRkby8ccfn7ZffHx8pq9ZkKjSSaSgePPNtPfHxhaM2f9ECpLApNPphtUladnS3547V99LERERETnjKekkUlD4lutM5dAhb5idiITGyZOwfr1/u0mTzJ13/vlQrpzX3rwZfvkl9LGJiIiIiMpEIp8AACAASURBVBQgSjqJFBSbNqV/LDY27+IQKew2boQTJ7x29eqQyRVIKF4cAldUef/90McmIiIiIqeVNCyuXbt2+R3KGU9JJ5GC4Phx2LIleF+1av62kk4ioRM4tC6zVU5JrrvO337xRfj229DEJCIiIiJSACnpJBLutm+H2rUhcBnQJUvg8cf928uX531cIoVVTpJOV14JnTp57cREePbZ0MUlIiIiIlLAKOkkEu7Gj4edO/3bF14Ibdt6jyQ//5z3cYkUVjlJOhUtCmPH+rdXrQpNTCIiIiIiBZCSTiLhLmVCKWmi4nPP9X7BBVi7Fvbvz9u4RAqrwKRT06ZZP79pUyhSxGtv2QJHjoQmLhERERGRAkZJJ5Fwl/IX1h49vOeIiOAl2pcsybuYRAqznFQ6ARQrBnXr+rd/+y3HIYmIiIiIFERKOomEu61b/e2//hUGDfJvt2/vb2uInUjO7d/vH85aooQ3n1p2NG7sb69dm/O4REREREQKICWdRMKZc8Gr1k2aBJGR/u3zzvO3tYKdSM4FVjk1auQfJpdVjRr52+vW5SwmEREREZECSkknkXD2xx9w+LDXjoz0z+eUpFYtfzshIe/iEimscjq0Lklg0mnoUFi0KPvXEhEREREGDBiAmWFmFC1alNq1azN48GD27duXqu8LL7xAkSJFGD58eJrXcs4RExND586diYqKokyZMrRt25bnnnuOAwcOADBy5EjMjB5J05sEeP311zEzWrRoEbT/6NGjjB49mmbNmhEREUGFChXo1asXCxcuDOr30Ucfcemll1K5cmWioqLo2LEjM2bMyO5bE9aUdBIJZ4FD62rWBLPg49Wq+dtKOonkXOAw1ebNs3+dwOF1ABdfDEuXZv96IiIiIkKPHj1ISEggPj6emJgYPvvsM+6+++5U/caNG8djjz3GhAkTSExMTHU8Ojqav//97/z1r39l7ty5rFixgtGjRzNv3jw++uij5H5Vq1bl+++/Jz4+Puj88ePHUzvFNAzHjx/n0ksv5c033+Txxx9nzZo1zJ07lypVqvCXv/yFzz77LLnvt99+y8UXX8ysWbP45Zdf+Otf/8q1117L999/n8N3KPwUze8ARCQDgUmnwKqmJEo6iYTWggX+9vnnZ/86XbtCq1awYoW3ffgwPPssTJmSo/BEREREzmQlSpSgatWqANSsWZM+ffowYcKEoD4LFixgz549jBw5kilTpvDFF1/Qq1ev5ONTp05l8uTJTJ8+neuuuy55f926dbniiivYH7AqeMWKFencuTNvv/02o0aNAmDFihWsXr2au+66izlz5iT3femll/jf//7H4sWLadu2LQB16tRh/Pjx/P777wwaNIj4+HgiIyN5+eWXg2J+8sknmTVrFp988gl/+ctfQvNmhQlVOomEs8D5nGrWTH28UiU4y/c13rcPjh3Lm7hECqPDh2HZMv92x47Zv1bx4vDLLzB6tH/f+vXZv56IiIhILkkaspYfj5zYsGEDs2fPplixYkH7Y2JiuOmmmyhWrBj9+/cnJiYm6PjkyZNp3LhxUMIpULkUU5oMGjSIiRMncurUKcCrourduzdRUVGprtujR4/khFOghx9+mN27d/PVV1+l+3oOHjxI+fLl03/BBZSSTiLhbNMmfzutpFORInD22f7tHTtyPyaRwmrxYkgqv27ePPUcall11lkQHe3fVjWiiIiISI7Mnj2b0qVLU7JkSRo0aMCvv/7Ko48+mnz80KFDTJ06lWjfv8FuueUWPv/8c3YE/J60bt06mjZtmul79uzZkxMnTjB37lyOHTvG5MmTGThwYKp+a9eupVmzZmle45xzzgFgTeD8oQFee+01tm7dmhx3YaKkk0g4++UXfzu9/zBqiJ1IaCxZ4m936hSaa/rKvwHYudOf1BIRERGRLLvwwgtZtmwZixYtSp6T6d57700+/sEHH1CzZk3atWsHQP369Wnfvj0TJ05M7uOcy9I9ixQpwq233sr48eP55JNPqFixIhdccEGafU9XvZXW8enTp/Pwww8zefJk6tSpk6XYCgIlnUTClXPBkxp36JB2v8CkkyqdRLJvwwZ/O52/UmVZiRJQsaLXPnUKdu0KzXVFREREQsQ5l2+PrIqMjKRhw4a0bNmSV155hSNHjjA6YDqDmJgY1qxZQ9GiRZMfCxYsYNy4ccl9GjduTFxcXJbuO3DgQD799FNeeumlNKuckq4bGxub5rFff/0VgEaBKxzjJZyio6N55513uOqqq7IUU0GhpJNIuNq0CXbv9tply0LDhmn3C6ykUKWTSPZt3Ohv16sXuutWr+5v6zsqIiIiEjJPPvkkzz77LNu3byc2NpaFCxfy5ZdfsmzZsuTHwoULiY+P57vvvgOgb9++rFu3LmiVukCBE4knadiwIe3bt2fx4sXccsstaZ7Xt29f5s6dy9I0Vix+7rnnqFSpEpdeemnyvqlTp9K/f38mTJjADTfckJ2XXyBo9TqRcPW///nb7dv7JwxPScPrREIjsNKpfv3QXbdaNVi50mtv3w5pTC4pIiIiIlnXtWtXmjdvzpgxYyhRogRt2rShR48eqfp1796dmJgYLrzwQnr37s0nn3xCv379GDZsGD179qRKlSrExcXxn//8hxtvvJEBAwakusYXX3zBsWPH0p3s+7777mPGjBlcddVVPPfcc3Tp0oV9+/bxyiuv8MUXXzBt2jQiIyMBbxhgdHQ0//73v7nwwguT55wqXrw4FSpUCN0bFAZU6SQSjvbuhccf92+3b59+Xw2vE8m5U6dU6SQiIiJSAD3wwAO88cYbvPTSS+lWDN14441MmzaNP/74AzPjvffe4+WXX2bmzJl069aNli1bMnToUC666CKuv/76NK8RGRmZ4epyJUqU4Ouvv+aOO+5g1KhRNG7cmK5du7Jz506+++67oOFzb775JidPnuS+++6jWrVqyY/0VtQryFTpJBKOXn/dv3Jd8eLQv3/6fTW8TiTnduyAY8e8doUK3pDWUFE1ooiIiEiOTZgwIc39ffv2pW/fvhmeO3DgwKC5mMyMO++8kzvvvDPdc0aOHMnIkSOzdLxkyZI8+eSTPPnkkxnGM3/+/AyPFyaqdBIJR6tW+duPPAK+JTbTpF9oRXIut4bWQfB3dPv20F5bRERERCSMKekkEo62bvW3u3fPuK+G14nkXG4mnWrW9LeT5nYSERERETkDKOkkEo62bPG3a9XKuG/g8LqdO725aUQka9au9bcbNAjttf/yF/9CAAsWwK5dob2+iIiIiEiYUtJJJNwkJgYPwalRI+P+ERFQrpzXPnkS9uzJvdhECqvVq/3tpk1De+1KlaBLF6/tHMycGdrri4iIiIiEKSWdRMLNzp1e4gmgcmUvqXQ6GmInkjNxcf52s2ahv/7VV/vbc+eG/voiIiIiImFISSeRcBM4tC5wLpiMaAU7kew7eRLWrfNvN2kS+nu0a+dv//Zb6K8vIiIiIhKGlHQSCTeBk4ifbj6nJFrBTiT7NmyAEye8do0aUKZM6O9Rr56/vXFj6K8vIiIiIhKGiuZ3ACLic+QIjBsHH33k35fZSicNrxPJvtyczylJjRpQrJiX3Nq1Cw4fhlKlcudeIiIiIiJhQpVOIuHiP/+Be++F+fP9+zS8TiT3BQ53y42hdQBFikDt2v7t+PjcuY+IiIiISBhR0kkkXCxenHrfpZdm7tzq1f3tzZtDE4/ImWL3bn87MIEbahpiJyIiIiJnGCWdRMLF/v3+9hVXeEmo887L3LmBQ4JiY0Mbl0hhF5h0qlw59+4TmHTasCH37iMiIiJSCA0YMAAz4/bbb0917JFHHsHM6NWrFwAjR47EzFI9qgb8gbFr167J+4sXL06DBg0YOnQox44dC7p24PmlS5emdevWTJgwId04W7VqRdGiRVm7du1pX9OJEyd49NFHadWqFaVKlaJatWr07duXzYWokEBJJ5FwEZh0euKJzCecwFvi3cxrr18Pf/4Z2thECjMlnUREREQKhFq1ajFlyhQOHz6cvO/kyZO8++671A6cygBo0qQJCQkJQY+VK1cG9bnttttISEhg/fr1PPfcc7z22muMHDky1X3feustEhISWL58OX369OG2225jzpw5qfotWrSI3bt3c8sttzBu3LjTvp4jR46wdOlShg8fztKlS/n000/ZsmULPXv25OTJk5l8V8Kbkk4i4eKPP/ztsmWzdm7JktCwodd2DuLiQheX5L9Dh2DfvvyOovDKq6RTYEXijz/m3n1ERERECqlWrVrRqFEjpk6dmrxv1qxZRERE0LVr16C+RYsWpWrVqkGPyin+rRcZGUnVqlWpXbs2119/PZdccglffvllqvuWK1eOqlWr0qBBA4YNG0aFChXS7Ddu3Dj69u3LbbfdxsSJE0+bOCpbtixfffUVffr0oUmTJnTo0IH//ve/xMXFEVdIfqfT6nUi4SKw0qlcuayf36IFrFvntWNjoW3b0MQl+Ssuzl/19v33WauAk8zJq6RTt27ehOKJid7w2d27c/d+IiIiIplggQsZ5TGXIlGUGYMGDWL8+PHcdtttAMntDTmsJF++fDk//PADdevWTbdPYmIi06dP5/fff6dYsWJBx44cOcIHH3zA/PnzOffcc4mMjGTmzJlcc801WYrjwIEDAJQvXz7LryEcqdJJJBw4F5qkU5JVq3Iek4SHW2/1hkv++Sc8+mh+R1M45VXSqVw56NTJazsHafx1TEREREQy1rdvXxYvXsy6devYsWMHs2fPZsCAAan6xcXFUbp06aDHzTffHNRn7NixlC5dmhIlSnDuueeye/duHn744VTXio6OTu7Xp08fKlasmGpuqSlTplCrVi3atGmDmdG/f/9MDbELdPz4cR588EGuvPJKamZ2JfMwp6STSDg4ehROnPDaJUpARETWr9Gsmb+diUnrpID4+Wd/e8GC/IujsDpxwp/wPessqFAhd+93+eX+9jff5O69RERERAqh8uXLc+211zJ+/HgmTpxI165dU83nBNCgQQOWLVsW9HjxxReD+vTp04dly5axYMECevfuzR133MH111+f6lrPP/88y5Yt46uvvuLcc8/llVdeoWHS9CY+48aNIzo6Onk7Ojqa2bNns337dgAmT54clAD7/vvvg84/efIk/fv3Z//+/bz99tvZfn/CjYbXiYSDwCqnrM7nlCRwqfc9e3IWj4SHlGPAC8lfO8JK4HelYkUv8ZSbzj/f316+PHfvJSIiIpIJ2Rnilt8GDhzIrbfeSunSpXnqqafS7FO8ePFUiaGUypYtm9xn0qRJNG/enAkTJqSqnKpatSoNGzakYcOGfPjhh7Rt25a2bdvS1Ddn5+rVq/nhhx9YsGABw4cPTz4vMTGRt99+m+HDh3PVVVfRsWPH5GM1atRIbp88eZKbb76ZlStXMn/+fCpWrJil9yOcqdJJJBzkdGgdBA8LUtKpcPj11+Bt5/InjsIsr4bWJWnd2t9etSp1YlFERERETqt79+4UL16cPXv2ZHnOpPQUK1aMYcOGMXToUI4cOZJuv4YNG3LdddfxyCOPJO8bN24cHTt2ZPny5UGVVSNHjmT8+PE454iKikpOXDVs2JCSJUsCcOLECfr06cOKFSuYN28eVQOLCQoBJZ1EwkHgynXZTTpVquRvK+lU8O3dCz16BO9LSMifWAqzvE46Vajgr1g7dgzWrMn9e4qIiIgUMmbGihUr2LhxIyVKlEizz8mTJ9mxY0eqR0b69u2LmfHqq69m2O/BBx9k5syZLFq0iBMnTvDOO+/Qt29fWrRoEfS48847iY+PZ968eenGeOONN/LTTz/x/vvvY2bJcf7555+ZezPCnJJOIuEgFJVOgSWYv//urZAlBdf48cEJEYBDh+DgwfyJp7DK66QTBFc7aYidiIiISLZERUVRpkyZdI+vWbOGatWqpXqczKDSvHjx4gwZMoTnnnuOgxn8u7tly5b06NGDxx9/nM8++4zdu3enORdUtWrV6NKlCzExMWleZ+vWrXz66ads376d8847LyjOKVOmZPDqCw7N6SQSDkIxp1OxYl7Cav9+OHUK9u0Lrn6SgiW9yeATEiAqKm9jKcwCqwLz6vvSujXMmuW1ly2Dvn3z5r4iIiIiBdiECRMyfXzkyJGMHDkyw/7z589Pc/+wYcMYNmxY8rZLZ4qLLwNWIj516lS69/nuu+/SPVa3bt10r19YqNJJJByEotIJNMSuMNm0Ke39CQn+lQ4l5wK/e7m9cl2Stm397cWL8+aeIiIiIiL5QEknkXAQijmdQEmnwmTzZn+7USN/u2tXqF4dZs/O85AKpVBUGWZV+/b+9uLFXmWiiIiIiEghpKSTSDhQpZMEci446dSpU/DxPXvg2mshNjZv4yqMQpXwzYpateDss732wYOaTFxERERECi0lnUTCQaiqLQInQk45CbWEv8RE+OYbb76fpNUqypSBc89N3ffoURg3Lm/jK4zyo9LJDDp08G8vWpQ39xURERERyWNKOomEA1U6ydat3lw/3bvDlVf699euDf36eUOyzjkneOWzrVvzPs7CJj8qnSB4iN2yZXl3XxERERGRPKTV60TCwe+/+9s5mcxYSaeCKyYGVqxIvb92bW8oVlI1zDffeIkpgJ078y6+wio/Kp0A6tXzt3fsyLv7ioiIiIjkIVU6iYSDUCWdAofXKelUsKT3edWuHbydNBcQKOkUCqGqMsyqKlX8bX2OIiIiIlJIKekkEg5ClXSqWNHfVtKpYEmawymlwOF0oKRTqAUOr8vLSqfAz3HXrry7r4iIiIhIHtLwOpFwEKqkU/ny/nbgL9MS/o4c8bdHjfJ+JooXh1tuCe5XoQIUKeJNOr5/Pxw7BiVK5G2shYkqnUREREREco2STiL5LSl5AN6qVjn5xTfw3H37chaX5K3ApFOrVnDNNWn3O+ssL2GRkOBt79oFtWrlfnyF0dGjcPy41y5WDCIi8u7egUNh9+6FkyehqP6XLCIiIiKFi4bXieS3wORQuXJeFUt2BSadAis4JPwFDq+LjMy4r4bYhUbKKiezvLt30aL+4bDOaTisiIiIyGkMGDAAM+P2229PdeyRRx7BzOjVq1fQ/l27dhEREUHt2rU5depUqvPq1q2LmWFmlCxZkqZNm/L888/jnMu113GmUdJJJL+FamgdKOlUkAVWOpUsmXHflEmnn3+GOXO85IVkXn7N55RE8zqJiIiIZEmtWrWYMmUKhw8fTt538uRJ3n33XWqnXIAHmDBhAldeeSURERHMmTMnzWuOGDGChIQE4uLieOihhxg2bBhjx47Ntddwpkk36WRmtbP5yNb4ADO738xizWyVmb1vZhFmVsHMvjKzdb7n8gH9h5rZejNbY2aXZeeeImEhlEmn0qX9lVJHjviHDkn4y26l04wZ0KkT9OwJb72VO7EVVvk1n1MSzeskIiIikiWtWrWiUaNGTJ06NXnfrFmziIiIoGvXrqn6jx8/nltuuYXo6GjGjRuX5jWjoqKoWrUqdevW5fbbb6dVq1Z8+eWXufUSzjgZJYjigaz+2dwBHYClWTnJzGoA9wLnOOf+NLOpwE3AOcBc59wzZvYY8BjwqJmd4zveHKgOfG1mjZ1ziVmMVyT/hTLplDQn1N693vb+/cG/2Er4ym6lU+BfYf75T7jzztDGVZip0klERESE+TY/3+7d1XXN8jmDBg1i/Pjx3HbbbQDJ7Q0bNgT1+/7779m7dy89e/akRYsWjBkzht27d1M5cG7NAM45vv32W+Li4mjUqFGW45K0nW543TPAnZl8DAZyMiFGUaCkr1IqEtgOXA1M9B2fCCTNrHs18IFz7phzbiOwHi/ZJVLwhDLpBBpiV1AFJp1OV+lUtWra+wN/luT0wqnSqX9/OHQo72MQERERKWD69u3L4sWLWbduHTt27GD27NkMGDAgVb+YmBj69OlDsWLFqFevHh07duSdd95J1W/48OGULl2aEiVK0K1bN5xz3HvvvXnwSs4MpxsK96lzblFmLmRmRYA3sxOEc26bmf0b2Az8CXzpnPvSzM52ziX4+iSYWdK/0GsAPwVcYqtvX1pxJSXFOPvss5k/f352Qgwrhw4dKhSvQzw1fvqJpDz6tj//ZF0OP9u2RYtSxtdeMncuB7dvz9H1CpNw/u6cf+AAxX3tH5Yu5UR8fLp9IytWpP1ZZ2EpJkM8WLUqS8L09YWjaosW0cTXTjhyhDV5/N7VPnKE+gHbvz34IFtuvjlPY8iMcP7eiIQrfW9Esk7fm7xTtmxZDh48mN9hAGQpjhMnTnDy5EmKFi1Kr169ePPNNylbtiwXXHAB5cuXTz5+8OBBDhw4wLRp0/jss8+S73HjjTfy2muvcWfAyADnHEOGDCE6Opo9e/YwevRoLr74Ylq2bBk271FGEhMT8zTOo0ePZvl7mlHSqRvwa2Yv5JxLNLNuwJosRQD45mq6GqgH7Ac+NLP+GZ2SVgjpxDUWGAvQrl07l9Y4z4Jm/vz5aY5XlQJq3rzkZo1WraiR08+2Vi1Y430Nz2vQAPSzkiysvzsnTiQ3u1xyCURFZdx/1Sr497+DdkUdPx6+ry8cffttcrNaq1ZUy+v3rlo1CJhboMHOnTQIw88vrL83ImFK3xuRrNP3Ju/ExcURFfBvzewMccsPxYoVo2jRokRFRXHXXXdx6623Urp0aZ566imioqKCjk+ePJkjR45w6aWXBl0jMTGRFStW0KVLFwDMjGrVqnHuuecCcN5559GoUSMuvPBCunXrluevMasOHjwY9FnmtoiICNq0aZOlc9IdXuec+9Y5l6Vaf985h0/fM5UewEbn3G7n3AngI+B8YKeZVQPwPSdNerEVqBVwfk284XgiBU9uDq/bty/n15Pc51zwROKnm9MJYPRo+PvfoWVL/749e0IfW2EWOO6/Xr28v3+TJvDNN/7tlSvzPgYRERGRAqh79+4UL16cPXv2cM0116Q6Pm7cOIYMGcKyZcuCHldccUW6E4oDlC9fniFDhnD//ffjtDJ0SJxuTqe8shnoZGaRZmZAdyAOmAHc6utzK/Cprz0DuMnMSphZPaARkKlhgCJhR3M6yfHjkDRUrlgxKJqJRUAjIuCVV2D5cn//w4eDk1eSsY0b/e38SDoBnH++f8XJDRugAJRxi4iIiOQ3M2PFihVs3LiREiVKBB1bsWIFixcv5o477qBFixZBj+joaKZOnZrhkLR77rmHNWvW8OGHH+b2yzgj5DjpZGaPmdnQnFzDObcQmIa36t1KX1xj8SYyv8TM1gGX+LZxzsUCU/GG/80G7tHKdVJgBVanVKyY8+uVL+9vK+lUMGRlEvGUzKBSJf920sqFcnqBSaf69dPvl5tKlICmTf3bsbH5E4eIiIhIARMVFUWZMmVS7Y+JiaFRo0a0atUq1bFevXpx6tQp3n///XSvW7lyZaKjoxk5ciSnUsyhKlmXiT+nn9YYvDmW/pWTizjnngSeTLH7GF7VU1r9nwaezsk9RcLC7t3+djrLd2aJhtcVPFkdWpdSpUqwY4fX3rMHatYMTVyF2bFjsG2b1zaD2rXzL5aWLf3JphUroFOn/ItFREREJExNmDAhR8cBSpUqxZGAP/jGp7N4z9ixY7MQmWQkFMPrGvseIpIdgZVOgRUr2aXhdQVPTiqdIPjnRvM6Zc7mzd5cWuAl6YoXz7h/bgr8K9zSpfkXh4iIiIhIiOW40sk5t+H0vUQkTc6FvtJJw+sKnlBUOiVR0ilzwmE+pyTt2/vbizQ9oYiIiIgUHlmqdDJPUzPrYmalcisokTPG4cNw9KjXjoiAUiH4WgXOC7VrV/r9JHyo0invhVvSycxrr1gR/PMgIiIiIlKAZTrpZGZ3AduBWOA7oIlv/0dmdk/uhCdSyKUcWpf0i2dOVK/ubyfNWSPhTUmnvLdzp79do0b+xQFQtqx/MvHERA2xk/By9Kh/KKqIiIhIFmUq6WRmA4HXgS+AfngThyf5Ebgx9KGJnAFCPbQOgieR3rZNvywUBBpel/cCqwBD9d3LiY4d/W0NsZNwMXOmVz3bpo0q8EREChmn3xEki7K7kl9mK50eAl50zg0EPkxxbDXQNPUpInJauZF0KlvWXy1z+DD88Udoriu5R5VOeS/wu1elSv7FkaRtW3971ar8i0MkUN++3n+fli+HcePyOxoREQmRiIgI9u7dq8STZIpzjuPHj7Nt2zZKZWM6mMxOJN4A+DydYweBcukcE5GMhHrlOvCG6NWsCWvXetvbtgWvaCfhJ5SVTnv35jyeM0FuJHxzolkzfzsuLv/iEEly6hQcPOjf/v57+Pvf8y8eEREJmZo1a7J161Z2B/57SAqko0ePEhERkev3KVq0KGXLlqVSNn5nzWzSaQ9QJ51jjfHmehKRrMqtX3xr1AhOOjVvHrprS+ip0invBQ6vC4dKp3PO8bfj4rxhsaGY400ku377LXh7xgx48UWvgvbii+H88/MnLhERybFixYpRL78XUpGQmD9/Pm3atMnvMDKU2eF1M4EnzKxuwD5nZhWA+4FPQxyXyJkhMEEQ6qRTkq1bQ3ddyR2BSSfN6ZQ3wq3SqVo1KFPGa//xB+zYkb/xiCxZErx97Bg88AA88QR06QKjR+dPXCIiIlKgZDbp9DiQiLdy3WzAAS8CcXiTio/KlehECrvAX3xDNbwOUk8mLuEtcHhdKCqdND4/Y6dO5c7Q1pwwCx5i9+uv+ReLCJx+FcURIzTpvYiIiJxWppJOzrndQDvg30AUsAkoBYwFOjnn9udahCKFWW6toKVKp4Ilp8PrIiMhaSz30aNaZep0fv/dSzyBN99Z8eL5G08SJZ0kXDgHs2en3t+sWfD35euv8y4mERERKZAyW+mEc+4P59yTzrlOzrn6zrn2zrknnHNaGkskuzZv9rcDq5NyKvBaCxeq8iXcBU7Wm53hdWYaYpcVuZXszalWrfztuCVGmwAAIABJREFUn3/OvzhEpk6FlSv92+efD//4h1f99Oqr/v0rVuR9bCIiIlKgZDrpJAVIbCzccgtMmpTfkcjpxMf723XSm6s/Gy64wF/5snw5zJkTumtL6G3Y4G/Xrp29ayjplHmBw1rDYRLxJIETM//wQ/7FIWemXbtg9Wp45x246Sb//ttv934eX3rJ+/9Ky5b+Y1OmwPDh/spBERERkRRynHQys8fMbGgogpEQuf9+ePddiI7WX8vD2R9/eA/w/iEfyl9+K1WCO+/0b8fEhO7aEnrr1vnbjRtn7xpKOmVeuFY6tWnjTxZv2KDJxCXv/Pe/ULWqN3zu1lv9+81gyJDgvi1aBG//858wfXruxygiIiIFUigqncb4HhIuvvrK3x6qfGDY2rTJ365TJ/TLo/fu7W9v2RLaa0vonDwZvDR5w4bZu46STpkXrpVOxYtDhw7+bVU7SV5ITISnnko9DLtUKfjiC2jdOnh/6dKpr/GpFjEWERGRtIUi6dTY95BwNHeuJhUOVymTTqEWmIT4/ffQX19CIz7eSzyBNwF8Wr/QZUbFiv62kk4ZC9dKJ4BOnfxtzZcjeeHbb2H79tT7P/kELrss7XOuvjp4e9my0MclIiIihUKOk07OuQ3Oud9O31PyTMoExuef508ckrHcTjpVqOBv790b+utLaKxd629nd2gdqNIpK8K10gmgXj1/e9u2/ItDzhzvvedvX3cdjBgBH38MPXqkf85ddwVvx8bqjxsiIiKSpkwlncysp5mVyu1gJEQSE4O3p03LnzgkY7k1iXiS8uX97f37U/9cSHgIVdIpMHmi4ZQZC+dKp8CVJ7duzb845MzgXPAfph58EEaNgmuuyfi8yy/3kkyBKy7++GPuxCgiIiIFWmYrnT4H9pnZj2Y2xsy6m1lEbgYmOZA0VCfJzJnw55/5E4ukL7DSqW7d0F+/aFEoW9ZrO+eftFzCy8aN/nZ253MCaN7c39awrIyFc6VTjRr+tpJOkttWr4aEBK9drhx07Jj5c8uXh4sv9m8vXBja2ERERKRQyGzSqTHwd2ATMAj4Ci8JNd/MnjSzC3MrQMmGlEmnw4e9uRkkvAQOgapaNXfuoSF24W/fPn87J1U3gZP9xsbC8ePZv1Zhp0onEc/XX/vb3bpBkSJZO799e3978eLQxCQiIiKFSqaSTs659c65/zrnbnbOVQNaAA8DicAI4JtcjFGyKmXSCeCtt/I+DsnYgQP+dlJFUqgFTi6t+TbC0/79/na5ctm/Trly/oq548chLi5HYRVq4VzpVKmSt4odeNWJhw7lbzxSuM2c6W9nNIdTegKTTj//nHoFPJH/Z++8w6Sosjb+3snkzJAzkoOIIoiCGEAR42JY04pxXV1d/cwB46qsa9igrjnumhNgQhAkgwiScxwYkDwwMMOE+/1xpry3erp7qqcrdp3f8/TDrZqaqsN0d4X3nvMehmEYJvQkZCQuhKgphBgO4AoAVwIYAmA/gAlxf5Fxl2ii0w8/AGvXuh8LExtddKpb15lj6JlOLDr5E7tEJwDo21eNuZtUdMrKzFl/ujDrB4QwZzuxmTjjFIsWAd99R2MhgBEjEt9Hp07qvLV7t9mjjmEYhmEYBtaNxB8RQswAsBfAxwD6APgQwAAAjaSUVThOMq6ii076zOUHH7gfCxMbt0UnLq/zJyw6ucvu3Sobo2FDIDPT23iiwSV2jBs8/bQaX3AB0KFD4vsQAujfXy137cqfWYZhGIZhTFjNdLofQF8A/wDQQUp5hpTyb1LKBVJyLrXv0EWnyy5TY+5i5y/cEJ24vM7/2Ck69eypxpxxEB0/+zkZsJk44zTFxcCXX6rlu++u/r5OOcW8/Pbb1d8XwzAMwzAph1XR6RYA3wEYAyBfCLFACPE3IcQZQojazoXHJIyUZtHp3HOB7GwaL1rEJXZ+4cgRoKiIxunpQI0azhyHy+v8j52ik979bt265PaVqvjZz8mgTRs1Zm8uxgmmTQMOHKBxhw5Av37V39ettwK9e6tlNhRnGIZhGEbDqpH4P6WU5wNoDOA4AO8B6AbgfwD2CCFmOhcikxDl5WqclkYG1XqJHbc09gfGzT5AWU5COHMcLq/zN6Wl6rMgBFCnTnL769hRjdevJ/8ixkwQMp2OP16Np071LAwmhRk/Xo3PPju5a1BODvDOO2qZRSeGYRiGYTQSMhKvKKVbCuBnAAsBrASQAeD4eL/HuIie5ZSRQf927qzWbdvmbjxMdNworQO4vM7vRHYwTEvolFyZ2rWB3Fwal5QAW7Ykt79UJAii00knKRHgp5/MIjXDJIuU5tK6UaOS32f37iQ+AXTe2bEj+X0yDMMwDJMSWDUSHySEuF8IMRnAPgCTAVwPYAuAPwHo4VyITEJEE52aN1fr8vPdjYeJjluiE2c6+Rs7S+sMuMQuPrrw3qKFd3HEo2FDVa5UVgZMn+5tPExqsXgxsHkzjevVA048Mfl9ZmQARx+tlhcsSH6fDMMwDMOkBFan1WcAuA3AAQD3AOgrpWwqpRwtpXxRSrnSsQiZxIgmOukPVpzp5A900SnZkqp4GFkvALde9yNOi07s4VYZ/RyoG3b7jWHD1PjVV72Lg0k99NK6M86wr4Oj3sWOS+wYhmEYhqnAquh0LIBGUspzpZTPSymXOBkUkwSc6RQMIj2dnKJdOzXetEm1imf8gROik+7rxKJTZXTx1a+ZTgBw1VVq/PnnwKpV3sXCpBZff63GZ59t335ZdGIYhmEYJgpWjcQXVPg5MX5HF53S0+lfznTyH256OtWsSeMDB4C9e507FpM4TohOHTqosVFCwyiCkunUqxcwciSNpQQmTPA2HiY1KC42C0KnnGLfvll0YhiGYRgmCkm61gJCiBOFECfZEQxjA1YynVg/9B63RCchgLZt1fKmTc4di0kcJ0QnXUjhksrKBCXTCQBOPlmN+bvL2MGiRcCRIzTu2BFo2tS+fXfpAtSqReP8fGDpUvv2zTAMwzBMYEladAIwBcAPNuyHsYNoolO9ekCNGjQuLOROSH7ALdEJYNHJzzghOnFmY2wOHlTfvawsc3dHP9KmjRpz1hpjB3PmqPHAgfbuOz0d6NdPLffqxRlPDMMwDMPYIjpdDWCMDfth7CCa6CQE+zr5DRadGMAZ0Un/rm/bxpmNOpGd64TwLhYrsOjE2M3s2Wp8/PH27/+448zL//uf/cdgGIZhGCZQJC06SSnfllK+ZUcwjA1EE50Azn7wGyw6MQCwZ48a16tnzz7r1FEdEYuL2cdLJyh+Tga66MTfXcYO9MwjJ0Sn2283Ly9bZv8xGIZhGIYJFHZkOjF+IpbopGc/bNjgXjxMdNwUnSI72DH+YcsWNbZTBGFfp+gETXTKzVXt7PfsofJAhqku+/cD69bROCMD6NnT/mM0bw6sWKGWWXRiGIZhmNBjWXQSQjQUQlwuhLhXCPFIxOthJ4NkEqCsTI110alvXzWePNm9eJjK5OUBb7yhlt0UndaudfZYTGLoIqCekZYsnNkYHf3vHQTRKS0NaN1aLesiJcMkyuLFaty9O5Cd7cxxOnUizzSArnf79ztzHIZhGIZhAkFG1ZsAQojTAXwCoFaMTSSAsXYFxSRBrEynM84A7ruPxt98Q+JUerq7sTHEPfeYl41SKKfo0kWNV68GysvpYZbxHhad3GX9ejXu2NG7OBKhTRsV9+bNQLdu3sbDBJeFC9X46KOdO05GBl13liyh5eXL7TctZxiGYRgmMFh98nwGwEIAfQBkSynTIl6sXviFWKJT375As2Y03rMHmD/f3bgYhXEjbnDCCc4er2FD1Ra7qIgNif3C/v3KSDwnx97W5Sw6RUcXnTp08C6ORNB9nTZu9CwMJgVYtEiN9exnJ+jRQ425xI5hGIZhQo1V0akdgEellEuklCUOxsMkSyzRSQhgxAi1PG2aezExZnTz6AULnM90AoCuXdV45Urnj8dUTWSWk52d1HTRiT2dFEEUnY46So0jBWuGSYQFC9TYyUwnwOwXpYtdDMMwDMOEDqui00IALarcivGeWKITYE5v128+GXfRu4m5VeLDopP/cKq0DgDat1djFiqIkhKV5SeE/X9zp+jXT41//tm7OJhgc+AAsHQpjdPSzJ8rJzjmGDXmzGqGYRiGCTVWRafbANwphOCifL8TT3TSbwL1tsmMe5SUqA5UaWnuZDkBLDr5ESdFp2OPVeMFC8znhbCyaRP5mQFkIp6T4208VtHFgUWLzM0iGMYq8+erz3/Pns5fe/r3V+NFi4AjR5w9HsMwDMMwvsWq6LQAwGQAM4QQBUKIzREv7sPuF+KJTj17qo4yGzaYy7wYd9CznBo0cM/Qm0Un/6H78+gdBu2geXPV9ezwYfZUAYJZWgcAubmqXPLwYWDVKm/jYYLJ7Nlq7Iapd+PG6nt25Ii5cx7DMAzDMKHC6hPv0wBuArAIwHiQAKW/pjgSHZM48USn7GygVy+1zCV27qMLfQ0auHdcFp38R16eGhsCkZ0cd5waz51r//6DxoYNaqyXHwYBLrFjksVt0QkwZ1xyiR3DMAzDhBarotMfQEbix0gpL5VSXhX5cjBGJhHiiU6AucSOvV7cR890atjQveO2aaPKiXbsMMfBeIPeVa5lS/v3P2CAGs+bZ//+g4b+93ZC5HMSvROYnrHFMFY4dAiYos0NOt0x1UAvsfvlF3eOyTAMwzCM77AqOkkAPzoZCGMTVYlOevvtnTudj4cxE1le5xbp6eYuWFyi4z16V7kWDvRp0LNjDAPhMLN9uxo3a+ZdHNVBP28bZugMY5VJk6g0EwC6dQM6dXLnuLpYumKFO8dkGIZhGMZ3WBWdPgJwhpOBMDZRlejUuLEa79rlfDyMGb28zs1MJ4BL7PyElM5nOukty5ctUybCYSU/X42bN/cujuqgZ2Zt2eJdHEww+fxzNT73XPeO262bGvM1h2EYhmFCSxRVIipfA3hWCFEPwDcAKtXmSCnZ18kPsOjkb7zKdAJYdPITe/cCRUU0rl3bmU5STZsCjRoBu3dTx8TNm+03LA8SuujEmU5MmJg1S41HjXLvuG3aADVqUJbVr7/SpIvbky0MwzAMw3iO1UynzwC0A3A1KOvp+4rXJO1fxg+w6ORvONOJAZzPcgIAISpnO4UZvbwu6JlOUnoXCxMsjhwB1q1Ty336uHfstDSgSxe1zCV2DMMwDBNKrIpOJ8d4DdP+ZfxAVaJTo0ZqzKKT+3iZ6aR7OukPIYz76KKTE35OBrroFGZfp/LyYHs6NWgA1KxJ48JCbgTAWGfdOqCsjMZt26rPkVvoJXYsOjEMwzBMKLFUXielnOZ0IIxNcKaTv9EzndwWnfRsCd3EmnEf/e/vVKYTYDbyDbPotGePOjfWq0clP0FCCCpVMjIUt2zhMiXGGnpWq57t6ha66LR8ufvHZxiGYRjGc6xmOjFBQRed0tMr/1zPdNqzR82AMu6gZyi4/dDYuDGQlaXiKCx09/iMwq1MJ720ZcMG547jd4JsIm7AZuJMdfBadOrVS41/+cX94zMMwzAM4zmWRCchRJoQ4gYhxGQhxGohxOaI1yanA2UsUlWmU2YmzfQDVHKyb587cTGEXuLjdqZTWpo5q4aznbzDrUwn3TicRSciaKV1Bm3bqvHbb3sXBxMs9JI2L0Snvn3VeNEi9iNjGIZhmBBiNdNpHIAXANQHMB/A5IgXd67zC1WJToC5xG73bmfjYRT5+cCCBTROS/PmAaBVKzXOy3P/+AyhZ6o4KTq1bk2fNYCyq4qLnTuWnwmyibjBJZeo8UcfAT/+6F0sTHBYtUqN9cxHt2jbVk107dnD1x2GYRiGCSFWRafLADwqpTxGSnmplPKqyJeTQTIJkKjoxL5O7vHpp2qW96STqKW927Do5A900alNG+eOk5lpfs83hTQpdccONc7N9S6OZBg2DLjoIrU8a5Z3sTDBQW8a0bmz+8cXonK2E8MwDMMwocKq6JQBgKdVgwCLTv7ls8/UePRob2Jg0ckfbN6sxk6KTgDQvr0ab9zo7LH8yq+/qrEXYq9dDBqkxuvXexcHEwz271fZzNnZzvrHxUMXnX7+2ZsYGIZhGIbxDKui08cAhjsZCGMTLDr5EymBhQvV8plnehMHi07ec/CgMpTPzgaaNHH2eOzrBOzcqcZBFp10ATGs7yVjHf0z0r69KrV1m/791fjbb72JgWEYhmEYz4ihSlTiNgDvCSFeBvAtgL2RG0gp2dfJD+jd6KyITvrDGOMcu3aRnwUA1KrlfHZLLFh08h69tK5VK+cfBHXRiTOdWHRiwoOeDdehg3dxjBhB3XTLyoDZs8lfzqusK4ZhGIZhXMfq005zAB0AXAPgIwDfV7wmaf8yfsBKppNuXMwlGu6gdxDq0sW7GWdddAqrAOE1bpbWASxUAKmT6aQLiJs3mycZGCYS3c/JS9GpcWNgyBC1rJeaMwzDMAyT8lh98n0DQGMAt4DK7E6ueA3T/mX8gBXRqVs3NV6+3Nl4GGLlSjXW//5u06mTGq9dC5SXexdLWHFbdGrdWo23bnX+eH5Ez3RyupzRSWrXVvGXlIT3/WSs4ZdMJwA45xw1njPHuzgYhmEYhnEdq+V1/QFcIaX82KlAhBD1AbwKoCcACWAMgFUAPgDQDsBGABdKKfdWbH8PgKsBlAH4s5SSjQIAa6JT9+5qvHw5+Q0J4WxcYUfPdPJSdGrYkGadd+0CDh+mUq+2bb2LJ4zo5XW6IOQUemZjGEUKKVOnvA6gzDUjc2vDBu9KdRn/4yfRqUsXNQ7jeYhhGIZhQozVTKfNAI44GQiA5wF8I6XsCqAPgBUA7gYwWUrZGcDkimUIIboDuBhADwAjALwghEh3OL5gYEV0at2aZswB8hliXyfn0TOdunb1Lg4AOOooNV692rs4wope4uaGYKCLTtu2kQgTJgoLgaIiGteoQZ5qQUYXD1at8i4Oxt+UlgLz5qllXfTxAvYTZBiGYZjQYlV0egzAXUKI2k4EIYSoC+AkAK8BgJTyiJRyH4BzALxVsdlbAM6tGJ8D4H0pZbGUcgOAtQCOcyK2wGFFdBLCLHxwiZ3z6CVVeombF+gPH/zQ6j7LlqmxGwJk7dpA3bo0Li5WLdTDQmRpXdCzOvv1U+PJk72Lg/E38+YB+/bRuGVL70UnXfzOywuf+M0wDMMwIcZqed1wAK0AbBRCzEbl7nVSSnllEnF0ALATwBtCiD4AFoD8o3KllPkVB8gXQhh1ES0B6KYAeRXrKiGEuA7AdQCQm5uLqVOnJhGmPzh48GDM/8dRmzfD6Amzat065MfYrmvDhmhWMV79xRfYZneQjIlB27Yhq2I8a+1aHNlbqQGka7TOzETHinHelClY27OnZ7G4TbzvjiuUleGkpUt/U/tn7N2LUhfiObZBA9QqKAAAzP/iCxR27FjFb6QOdZcvhyHTFNSogZ8Dfg2o1agRjq0Yl3z9NWZNngyZ7myir+ffGyZh2r3+OtpVjPP79MGqadO8DAeQEifm5CC9qAg4fBgzJkxAaZ063sbkMPy9YZjE4e8NwyROEL43VkWnwQDKARwAeS5FkuyUVQaAfgBullLOFUI8j4pSuhhEm6qOGoOU8mUALwNA//795dChQ5MM1XumTp2KmP+Pd975bdilRw90ibXd3LnAd98BAI4qLcVRKfB38S1lZcD+/b8tDjr7bCAz07t49u0DXn4ZANDq4EG0CtF7H/e74warV5MBNAC0aIHBZ5/tznE7dwY2bQIAHNuiBRCi9xwHDvw2rNuxo7fvvx0MGQI8+CCwdSsyDxzAkJo1gYEDHT2k598bJnHuu++3YfMxY9DcD+9f27a/ZdcObtcO6NXL23gchr83DJM4/L1hmMQJwvfGUnmdlLJ9Fa9kHSrzAORJKedWLH8MEqF2CCGaA0DFv79q2+sOvK0ATtYBYK28DuAOdm6yZ48qJWjQwFvBCTCX923c6FkYoWTJEjV2M8MszGbiqdK5zkAI4JRT1PLPP3sXC+Nf9OYVxx/vXRw6uq9T2M5DDMMwDBNirHo6OYqUcjuALUIIw3TgFADLAXwJwCjbuxLAFxXjLwFcLITIFkK0B9AZgOaYGWKsik6RHewY59CN2v3w0NuihRpv3+5dHGFk6VI1dlN00t/zsD3s5eercbNmsbcLErpwrHdDZBiAfNuMEu6aNc3ffy+J9HViGIZhGCYUxFQlhBBtAORLKUus7qzid7ZJKUur3LgyNwN4TwiRBWA9gKtAotiHQoirQR30RgOAlHKZEOJDkDBVCuBPUsqyahwz9bAqOrVvD2Rnk7Hw9u10g9qggfPxhRG/ZVoY2VYlJVR6VFgY/I5eQUEXndwsLQlzppP+/20Z1foveLTWEn1ZdGIiWbNGjTt18o95PnewYxiGYZhQEi/TaQOAo63uSAiRXvE7vasTiJRykZSyv5Syt5TyXCnlXinlbinlKVLKzhX/7tG2f1xK2VFK2UVK+XV1jpmSWBWd0tPN3Wz0VHzGXvRMp6ZNY2/nFkKYMz527PAulrDhVaaT/rAXtpLKbVrltV8yPpKFRScmHmvXqnHnzt7FEYl+HpozJ/Z2DMMwDMOkFPGMxAWAo4UQORb3lY7oBt+Mm1gVnQAqsVu8mMbLlwODBjkXV5jxW3kdQKKT8bC6fTvQIVlbNqZKiopUBoIQZl81p9GPpQtfYYAznZiwoWc6+Ul0OvFENf72W2DCBOCss7yLh2EYhmEYV6iqe90LCexLIPkudkyyJCo6Gaxc6Uw8jP/K6wCgeXM11j1vGOdYuZI6GQIk8rlZ0tixI1CjBnD4ML3fO3f657PoNKmY6RRpyFxeDqT5wqKR8QN+FZ169gSuuQZ49VVafustFp0YhmEYJgTEUyVOruY+V1Xz9xg70EWn9PT427Zpo8ZcYuUcfiuvA8zldWwm7g5eldYBdC7o2ROYP5+WlywBhg1zNwYvKC01n9t0sTXI1KwJNGxInTFLSuj/mCr/NyZ5/Co6AWbRaRXfLjIMwzBMGIgpOkkpp7kZCGMTiWQ6NWyoxnv2xN6OSQ6/ltcZsOjkDr/8osZumogb9O4dPtFpxw7KAgJI8M3M9DYeO2ndWp23t2xh0YkhpPS36KTHs2YNZ+kxDMMwTAjgK32qwaKT//BjeR2LTu6Slwe8/LJa7tPH/Rh0ocvwckt1UtHPyUD3ddKNo5lws2sXsH8/jWvXBnJzvY0nkoYNgUaNaFxUFL5umgwTRFavNt/LMgzDJAiLTqkGi07+orzc7Jfll2wEPY5XX+Ubf6cZNw4oKKBxx47AyJHux9BbaywaFtEpFf2cDHQR8aWXvIuD8Rd6llOnTtS0wG8cdZQar17tXRwMw1TN//5H3a5btgRuvRV48kng44+9jophmIDBolOqwaKTv1i0SHnKNG7sbseyeOiZTgBwww3exBEWlixR46eeIlNvt9FFiqVLlal5KqOb5PtF8LWLG25Q5/jp07kFPUP4ubTOQBed9HgZhvEfr71G/5aWAs8/D9xzDzB6NPDJJ97GxSgWLwZOOYXel++/9zoahokKi06pRjKik+F9wtjHV1+p8fDh/vGu6N0byM5Wy9OmkRcI4wx6JlnXrt7E0LixyvYpKgpHSda+fWpslPSkCm3aABdeqJZnzvQuFsY/BEF00uPiTCeG8S8lJcDs2dF/9uyz7sbCVKakBPj6a+CKK4ApUygD7bTT6D7z6ae9jo5hTPjkCZixRH4+cOml6HXXXcBFF0XfJhHRKTMTqFOHxuXlqvyHsY9vv1XjM8/0Lo5IatY0P5wcOMAdDJ1CSv94C4XN18nwtgGAevW8i8Mpjj1WjbkTGAMEQ3TSM52M5gYMw/iPRYuAQ4fU8vHHq/HMmeauvIz7PPEEPVvojWoAuh+44w5g7lxv4mKYKFRbdBJCNBFCZNkZDFMFUgL//S8azZtHmSnR0EWn9PSq98klds4hpfnBfuhQz0KJSuvWQP/+apnLHJxh/35101azprfiR9h8nfRMp/r1vYvDKfSsOd07jgkvuvjoV9FpyBDlNTVzJk94MIxfmTFDjS+7jLKefvc7te7rr92PiVGMHRv/59OnuxMHw1ggrugkhDhOCPGEEOJpIcTxFesuFEJsA7AdwH4hxD+EEJwx5QaRAlG0cqjDh9XYim8Mi07OsX27yh6rV8+fnjJs6Oo8kVlOXhr76qKT7jOVquiiUypmOrHoxOiUlAArVqjl7t29iyUeTZsCJ55IYymBL77wNh6GYaLzww9qfMIJ5n8BYN06d+NhFNGqU9LTyezdYMEC9+JhmCqIKRYJIU4HMAPAXwBcD+BHIcQfALwH4GcATwOYBuBPAK51PFIGyMmhTAmAbi4PHqy8jZ4GW6tW1ftk0ck59IfArl25i1BY8UtpHUDdrAzy8ryLwy1SvbyuTRu6LgDAzp18Dg87q1YBR47QuE0boEEDb+OJx/nnq/Gnn3oXB8Mw0SkuJp8gg1NPpX87dlTr1q93NyZGEa00uVkz4JJL1PLs2eFoGsMEgngZSvcCmAqgAYC6AP4N4AUAL0gpz5JS3iWlHAHgP2DRyT2qEokKC9XYEKiS2R9TfXTRqUsX7+KIB4tOzuMn0alpUzX+9Vfv4nCLVC+vS0szf4fZ1ync6CWzelajHznvPDWePNn8XWUYxlu2bCFxyXim6NRJTVp16KC240wn75g3r/K6Fi3o3G94+m7aRGXWRUXuxsYwUYgnOvUA8E8p5WEppQQwDkAOgPER230JoGPkLzMOEU8kkjLxTCe9oxOLTvYSmenkR1h0cp5t29TY6B7nFbm5arxjR+p3LEz1TCfALGiHoSMhExvdTLZPH+/isEKbNsoIv7QUGB95a8kwjGc89ph5wmzECDXWRadNm6jygnGfWKJTTg7Qo4dat2GDOWONYTwinujUCIDu7mhMi0cqE3tBmVCMG8QTnYqLqQsdAGRlVd29LnKiCDkOAAAgAElEQVR/u3cnHx+j0LMO/Co66Uaza9dyGq4T+CnTqVYtlQF55Ejqd6xM9UwnwCxksiFzuAlSphNgLrFjXyeG8Q/Ll5uX9e9qjRrqulNWRllRjPssWlR5nfG+XHmleT2XQTI+gA3Ag0Y80SnR0rqq9sckh5455Nfyurp1qQYcIBFi82Zv40lF9Bsyr0UnoHK2UyoThkwn/f0MQ8kko1izBsjPV8vLlqlxr17ux5MoZ56pxnPmeBcHwzBm9Mmyu+4CTj7Z/HMusfOWffuAjRsrr8/Opn//8hfg8svV+mjbMozLVCU6jRJCjBFCjAFwFQAJ4GxjXcX6UY5HySjiiUSJltZVtT+m+kTO/rRv710sVcElds6ilzx19EElsu7rlMqi05Ej6pyYlgbUru1tPE4RlveTMfPEE3TubtECuOACylo0rjkZGeamAX6le3fVZXfrVur4yjCMt0hpFp0eeKDyNvq9DItO7qNnteqUlqqxXhLJohPjA6qqv7ovyroHo6xLcWMQH2F3ppM++69nBTDJkZ+vTv5Nmqgbaz/SuTPw4480Xr0aGD7c23hSibIyc1qzHx4Ew5IZE5nl5MfukXYQlveTUXz6KXDvveblYcPUcseOQGam+3ElSkYG0LcvdVgCqL33yJHexsT4h927gTvuoGvo0UcDTz6pMjkY59i1S3XBrFcv+iS2bs2g+5cyzrNpEzBkSPSfnXuuGrdrp8YbNjgaEsNYIZ7o5OPUjBCjG39HejBVJ9OJRSdn2LRJjdu29S4OK+iZTmvWeBdHKrJli7p5y80F6tTxNh4jDoNUzozRz2ep6ucEcKZT2LjnHnr4jsSYOACAbt3ciydZ+vdn0YmJziOPAG+8QeNp0+j6+cgj3sYUBvQsp1jNT3Sjar2sl3GesWPNy9ddR+9Z797myQe9woIznRgfEFN0klJuivUzxkOczHRKdVNhN9G9kdq08S4OKwShvO7HH4Fx48gs/7XX/P83NdBFPH1m0Et0kSKVM2N0E/FU9XMCONMpTLz3XnTBCTB3MvJr44poHHOMGi9Y4F0cjP+YONG8/PTTwLXXAq1bexNPWLDS/IRFJ+/Qu5QCwPXXA/36Vd4uN5cyA4uL6XmxoIB8XBnGI9hIPGjY7enEmU7VZ8cO4NZbgeOOAz780PyzoGY6zZnjv8/B9OmUSjxxIvD998Dzz3sdkXV00ckPpXUAZzqlGpEiouRq95TlpZfUuHdvuvYY6DPZQRKd+vdX459+8i4OxhuWLwduvBF4/HESHTdtAg4fpnKgSK+gw4eBMWNUl2bGGayITh06ADk5NM7PB/budT4uhq7vumXD009HF5wA8rLkEjvGR8TMdBJCvJ3AfqSU8sqqN2OSxu5MJ1319pvY4GfKyoDBg5VJ9DXXAKNGKe+mIGU6de1KgsjatfQZeP554MFo1m0eETnbuXSpN3FUB8508o6wZDrVqEFlJwcOACUldPOvXyeY1EBKc0bBJ58AL79sznAy0CcS/E7XrnS/cugQsG0bPcA2b+51VIwbfPUVcP75lIkBAPffT/+mpSlBA6Dz28GD9B34/nvg44+BCy90P96wYEV0Sk+n7+6iRbS8bBndEzPOYmQsAXTevO22+Nt36gSsWkXj+fOBPn2cjY9h4hAv0+kyAOcCGAzgRAsvxg30h4np082z2rroVN1MJ54lt8bGjeauZAcOAF9/rZaDlOmUlmbuTmJ4KPiFyC4dQfKd0g02/ZLppD/QpXKdf1gynQAusQsDO3aobII6dcgsPFaphN8nOnTS08lM3IBL7FKbXbuAZ54BHn2U/LsMwUmnvNycuf/ww8Cf/6yWJ01yPs4wY0V0ArjEzgv0LKcOHapukKJ7PH3zjTMxMYxF4olOGwHUArABwEMAekop28d4dXAhVgaoPIM9ZIjKeKpOeV1ODpCVReOSEqCoKPkYw4DRmlrngw/UWBedgvAAcMklqtvRxo0kovmFyPr1TZuUObefKS8H5s5Vy7FSoN1Gv1FcupS+96mIngma6qITm4mnPsuXq3H37vSwEU10Skszi5BBQC+xY9EpdTlyBDjtNOD22ytnUw8cSJkzLVuaH6R79QKuugo47zy1bs4cd+INI3v3Ap9/rpbbx+kppWdUbmIbYFfQS+Q6WHj0HjFCjSdNSt37PSYQxBSdKoSkoQDWA3gOwHYhxNtCiFOFSNXe0wGgSROU6oLS9OnAWWfRA251yusANhOvDtFEpwkTSLTbtk09IKSl0Yy038nM9GcL3F276O+pU14ejNr0VatUZkLjxv75HDRqpITQI0eAFSu8jccp9O6ejRt7F4cb6CLD9u3excE4hy6+d+9O/0brhpmbC2TEa0zsQ3QzcfZ1Sl0efliVY+n86U/ArFl0LcrLownUvXvp9csvNGnQvz/dTwGUVbN7t/mel7GHv/9dXTvbtQNOPjn2tnpnu8j7NMYZIjOdqqJbN2W8X1AA/PyzM3ExjAXiGolLKadLKa8F0AzAtQAaAfgawBYhxJNCiC4uxMjoZGdj5d13k4mowezZwPvvVy/TCWBfp0R45hng1FOpbXUkhw5RW9+PPlJlikOGBCfLwniQAcyz6l4SmeVkEIQSO6MNOAAMGlR1GrSbHH20Gi9c6F0cTqKLTo0aeReHG+jZlEEQZJnEeO89s3eHka0YLdMpVotzP3PssWo8dy6X+acic+fG7rw4Zox5OSeH7pvq11fXzVq1KOsJoM9H48b0WeeHaHv58Uc1fvRRs7dWJCw6uY9urm9FdBLC3HAi0pyfYVzEUvc6KWWxlPJ9KeVIAK0AvAvgdgBPOBkcE51dgwfTw/gdd6iVDz5IRosG1c10YtEpNhs2UFr45MnmmnedESOoo53BRRe5E5sddOumxn7JfollGh4E0WnWLDUeONC7OKLBolNqofuF8U1lajF9OvCHP5jXGZNOqSI6demi7kN27kxtr7kwcugQcMUVquvc0KFkanzBBdSR0Wrp+fHHm5cLCigzh7EP/btX1X0Li07uo5cfW21OY2Q6AdGrNBjGJSyJTgAghMgWQlwI4FUAtwHYAYBdybzk3nvVjdq6dWb/mEQynVh0skasGbUbboi+PiODOrMEBV108kumk94FUBcO/BJfPHSjeb/4ORno8aTqTHFYRSf9c8cEn3HjgNJStfz73wOnnELjaOV1QRSd0tLMs/Hs2ZNa3H8/sHo1jWvXpmYl/ftTF7rrr7e+n5EjK697/317YmSo3N6YUBXCLFZEQz/X5Oc7FxdD7NmjylPT0ymD3gr6+5iXZ39cDGORKkUnIcRJQohXQCLT6wD2ARgJoLWU8mWH42PiUb8+MHy4Wp46VY3Z08k+9u2jbJAlS6L//NJLo7coHzMGaNLE2djsRC+v80umkz4rc+65ajx9uvuxJMq+fWrsN08hPdNp0SI1A51KhEl00v3CONMptdC7d86cSaV2hrdNqmQ6AeYsFhadUoeCAuCFF9Tys8+SV1B1OPPMyuuys4GysurtjzGTl6fuBVq2VE2GYtG4sfKP27OHGxE5zbRpqvS4f//Y3UsjadVKjTnTifGQmKKTEOIxIcQGAJMBtAdwC4BcKeXlUspJUnLRvS847bTo69nTyR4KCij1v18/MsGMRseOwCefmA0Xs7KA++5zJ0a7OOoo5Z+wfr0/biD0C+To0XSDCZBJt9/TuQ0TcQBo0MC7OKLRsqUSwg4cMJtTpgphEp3atqWZT4AeHPzw3WWSp7BQZXtmZJi9jwAWnRj/M348UFxM4169gKuvrv6+0tOB//zHvO7wYaBvX2o6wiSHXlpnRRhMSwOaNVPLnO3kLHpiQTyD90i4vI7xCfEyne4F0ADk3zQLQEcAdwkhHonyivE0zjiOHaITl9fF5ptvgF9/jb9Nbi55FEyZQj4FV11Fnex0c98gUKOGao9bXq7S4b1ETwXu3Nn8YPLdd+7Hkwh+Fp2ESG1fJynDJTplZanzjZRsJp4qrFqlxh07UpdRnWjldS1bOhuTU+iC2uLF5pJCJpj85z/AZZep5YsvTr6hxrXXUrafztKlwBNsMZs0iYpOAPs6uYluK2G1tA5g0YnxDVWV19UFcCWA+y28GC9o25ZekbCRePLs3g18+mn8bfr1U6UOAKW8vv56bDHQ7/ilxK6wELjuOrPo1LKleXbnnnvMnk9+orSUMogAusm2mgbtJqns61RYSP4UAHXfSeR8GFTY1yn10M/BXbtW/nnt2pXXBW2yw6BJEyWYFRWZBTcmeBQWArfcYl43enTy+xWCfM3uvde8fsqU5PcddnTRKdpzRTRYdHIP/X7XmCC2QvPmKhN6507OhGY8I6boJKVMS+CV7mbQTAR9+1ZeV91MJ/Z0IvLyqKzugw8q/6xdO+Czz4ArrwReecX10BzFL2bi775r/ts2bUqldZdeqlr4bt9OwpMf0cXbevXMwqRfSOVMJz3LyW9+Wk6h34Ry96/gs2ePOUtEPzcbpEfceqWlRd8uKKTyOSlsTJ+uyuoA4I9/tN5tywqXX25eXrbMfDwmcTjTyb9IaRadEplcSE83v09sJs54hA+fhJiEMdon6yQys8+eTpV58knzg6vOtdeSqfWbb/qvK1my+CXT6Y03zMtGenCnTmYh8OOPaebGb/i5tM5AP2+kWlZBmErrDPSZ6U2bvIuDsYfbbzcvR8t0iqRPH38K3FZh0Sl10DOPrr7abCZuB127kjBbowYtl5Sozl5M9Vi5Uo2tZtKw6OQOu3apDKV69RLPntdL7Lj8nvGIat2dCCHOFEL8nxDiUiFECOoWfE6vXpXXVTfTSe+4FVaKiyt7BgDA998Da9ZUTutOJfRZci9Fp0gzXN1X6+yzgQEDaHzkCDBqFDBpknuxWSEIolP79spfY/NmVY6WCoRddPJr2SljjaIiEtR1rJRs66JNENHjX7DAuziY5Jk8WY3POceZYzRoAJx/vlqeO9eZ44SBkhJzp8w+faz9XvPmasyik3NUN8vJQH9OnD8/+XgYphrE6153sxBicsS6NCHE1wDGAxgH4B0AC4UQAeoLn4JEZjo1bJhYS1r9oWzPHltCCjQTJkQX3045xeybkop06aLG69er9qxuo4s2QOWMshtvVOO5c4HTT6+cHeUlevz163sXRzxyclQr3fLy1CrJCqPopN+IcqZTsJk8GTh4UC2vXx+7K53uc/eHPzgaluPoZuIzZnA3rKCycqXyCUxLA046ybljHXecGr/2GlBW5tyxUpmVK1V5YuvW1svS9fMSf1+dI1nRyZioBbg7KOMZ8TKdzgMQWXMxBsBwAO8B6APgYgBNwUbi3hIphNx5J3Uzsop+cQl729lffwUeeaTy+gsvdD8WL6hXT6WrHzqkzLDdZscO8/Idd5iXL78cuOsuczen8eOdj8squmjp10wngDpiGaxb510cdqPPuIbF04nL61KHzz9X4zvvjF/q8tRTwKmnAo8/Dpx4ovOxOUmrVkqgKCsD3n7b23iYxJk2zZwxPXKkOZvebkaPVnYSixeT7QGTOHo5ayIZk1xe5w7Jik565+cffvBuQpkJNfFEp64AZkSsGw1gD4BrpJRLpJQfAvgbgDMdio+xQno6MGYMjXv3rtwxpCpYdCKxacoUIDfXnGJ8ww1kGB6WdrxCmNOlvZq52r5djX/5BTjhBPPPhSDfLf3hzE+f3SCU1wFm0SmVOp7pJvh69l4q06KFMpbesYM71ASZ6dPV+Kyz4m977LFUXpwqZd9XX63G77zjXRxM4hw6BJx3nnndX/7i7DGbNwfuvlstv/WWs8dLVVh08jfJik5duijx9+BBYPhwynBnGBeJJzo1BPCbxb0QIh3AYAA/SCl18495AFo5Ex5jmVdeoXTmmTNVdy+r6OUnu3aFTwFfupRO4qecYl7fsyfw4os0c9ahgyeheUKzZmqsiz9uUVysRJu0NKBHj9jb6hdfFp0SR8+STKVMp2XL1Dje5yeVyMhQ5ZIA+zoFkfnz6T00jP0zM4H+/b2NyW0uuEDdwyxbllplv6nOwoXma9/QofRymuuuU/6EM2fGbgLDxEYvuUpEdGrUSGWc798PFBbaGxdD6Cb51RGdIstcJ00CZs1KPi6GSYB4otMOANrTJ/oBqAFgdsR2ZQBSyIE2oKSl0YWidu3Ef7dGDWU8XlLiXUmVVzz0UPRWu5Hdg8KC15lOuml406aV24Lr+DVLLwieTkBqltdJac50CovoBHCJXdB54glg61a13LevKncOC7VqmX2qvvrKu1iYxPjpJzVu1Ij8MQ0xyElyc1X5UHk53RewEb11du1SJuxCAIMHW/9dv2THpzL5+cDUqTQWovoeaX//u3lZ71bIMC4QT3SaD+CPQoiMiuUxACSAyDuAngA4pzLo+PXh3WlKSoBPPqm8fsaM4JuyVhevbyD07Co96yoaepbe7t3+SRcOiqeTnumkCzVBJi8PKCigcYMGVX+GUgm9gcT69Z6FwVSTzz4zLx9zjDdxeM3IkWo8caJ3cTCJoXfFeuihxLooJ8uoUeblBx5w79hB57vvVIXD8ccn3nyDS+yco6wMuO02dW87dKg5ozkROncGHnxQLfM9AuMy8USnxwAcD2CzEGIZgOsBfC6ljJRGLwfAVvhBJ6yi0w8/RF8f6SEUJvwkOuXmxt82M1PVqZeXR+866AVBKa/r3l2lxq9fX7lrYBCJLK1zY6bdL3TurMarV3sXB5M4R6IkjDvZ9cvPnHGGGk+ZAhw+7F0sTNXs2UPdY/X7KbfLQg1fU4OZM7mTnVW+/lqNz6yGRS+LTs4xbhzw/vtq+fe/T25/ulVIqmS3M4EhpugkpVwE4CQA3wNYD2AsgEv1bYQQLUFleP91MEbGDSJ9ncJCtJrmW291Pw4/4bWnk965zkqWih8FUz0OP5fXZWcDvXqpZaPNdZDRb6S6dvUuDi846ig1XrPGuziYxIl8ABgwADj3XG9i8ZoOHdR3t6go9uQQ4z1FRZSRN2aMEhwyMqg01E1yc83CbUGBeQKCic2SJWo8bFjiv6+LTnl5sbdjEmfyZDXu0YMaGyVDKloqMIEhXqYTpJTzpZRXSClHSSkflVIWRfx8a8XPJjkbJuM4fnxwdwP9waxZM+Caa4D77/cuHj/gdaaTfsyqMp0Af3529Ztd/SLvR/QSnlTwwdCz3fTPRhjgTKfgovtrnHYaMHt2+PycdPQSO/Z18i8zZlQ2ez/vvMQb2thBZiYZ0RvcdhtZKDDx0f3/qtM0Ry/T/+675ONhFCtWqPHHH6vM9Oqiv79cXse4TFzRiQkRfnxwdwP9weyDD6gLYKL17KmGLjp9/737s8wbNqixbowcC799drdvV2botWoFS3TSjWCDyv79auznLDMn0EWn9euB0lLvYmESw+hYB1CWT5jKQqOhl/mw6ORfDINjgDoAf/kl8F8Pix90a4TJk4H77vMuliBQUKAmarKzqXlLopx/vjpfTZ7MJXZ2UVCg/paZmWZxr7o0b64mM/buTQ1LBSYwsOjEEPqDe1jazUppznTSH9jCjJ4qDVT2SnAaPeXXimDjN9FJb23buzd1lvQzuuikxx5UdNHJ8PsKC7VrK9G4pASYPt3beBjr6KXeXbp4F4dfGDxYdePdsIFLQfyKPil1ww1k6J2REXt7p4k0FH/lFSoBZKKjZzm1aVO9+5XWrcngGiBvzQ8/tCW00KNPRHTubM/3Sgj2dWI8w+dPQ4xr+O3B3WmKi4GLL1YPqLVrh6vLVTyaNgWGD1fLGze6a+SqXwStpHr77bOrCzd9+ngXh1W6dVPj9eujGxoHCb28LmyiE2D2dRo2DNi61btYGGt8+ikwfrxaNtq/h5msLGDIELX8/ffexcJEZ84cs1iqv19e0amTuUx83z7OlIuHLjpZySyPxYUXqvGMGdXfTypy+DBN3h59NDB6NBnvW0HvBmmnP6X+Pm/ZYt9+GaYKWHRiCL89uDvNuHHm2ZjOnbmcQSfyJs0oF3Oaw4fVQ3JaWuLldTNnOhNXIvzyixq7baZaHWrVoplKgLr96OWNQSTMmU4AcPrp5mU2YfY/L7ygxpdfbs4+DDOnnqrGLDr5iwMHgHPOUcvHHAM0aeJdPDr9+gEPPKCW9e5fjBm7RKeBA9V47tzq7ycVeeop6u64aBH5Mukln4WFVJKqZyXn5wNnnw386U9qnZ2iU8uWasyTUoyLsOjEEGESnaQEXn7ZvK46deypTFoazcoY6B3lnEQXPNq0odnuqtA/u19+SRd3L9EztXr08C6ORNCzY/SU7iASdtHp7rvNJbJeNANgrFNSQqbhBo895l0sfkMXnX74ga7djPcUFAD/+peajBICePFFb2OK5Pzz1TgVvAqdwi7RqUcPoGZNGuflsZgB0Pfk1luBhx82r3/pJeCqqygTOTeXxNuTTlIm7OPGmTNfAXuz5lu1UmN+nxgXYdGJIcIkOv3yS+W2rtVpE5vq6J3j3BKdEvVzAioLOx98YF881WH7djXWZ5T8jO4hk0qiU9iMxAESjG+5RS2z6ORvFi0CDh2icdu2JLYzRI8eQJ06NN69273rEBObAwco6+Lee9W6G28Ejj3Wu5ii0a0bkJ5O4w0bKKOEqYxdolNGhvkzENZsp+3bqVz67bdJKHr++ejbvfkmCen65/LTT+nfSG/Nyy83i6jJwplOjEfEdCUTQryewH6klPJqG+JhvCJMotOECeblW24hA0zGjC46OVFet20bcPvtNDt2yy1kur12rfq5VdHp+OPpovzOO2q/XiGlWXTS/4Z+Rhed9I6OQSTsnk6AuQMli07+5NAh4K23gDvvVOsGD/YuHj8iBAkchrfJihXsveg133xT+Zyil1b5hexsyuA1Ws4vX+4/Ycxrdu4Evv1WLVvx0IzHgAHAtGk0njvXXqEkCOTl0b2UMYkQyeDB8f2ujJ/pE39r1tjTtU6HRSfGI+JlOg0DcHICLybINGqkxrt3UweKVGX9ejV+8UXgueeAunW9i8ev6CWHTswwP/sseS28/jrNCN1yC/D44+rnusF1PISgdGSDpUuBxYvtjdUqe/dSuQxAnykj3dzvpGqmE4tO3L7arzzxBGWIHDyo1p14onfx+BX9OmAICIx3rFxZeZ1fxZyePdV46VLv4vArjz+urpedO5NolAz678+Zk9y+gsiECZUFp/r16Vy/fDkwZQowdixNqHbpQllQc+aornTLllGptSHqZmYC7drZHyeLTvYwdSqVThrv18qVwKOPBv8e2kFiZjpJKdu5GAfjNVlZ9JBcUEBmwvv3Aw0aeB2VM+jdGriUITZOl9dFPkD84x9qnJEBXHSR9X01aUKp9GVllG3Upw/wz38CN91kT6xW0bOcgjQjrz/YLVlCf8MgGuuXlKibvrQ01XI9bLCnk/+ZMsW83KxZ+DIDrKCfm6IJHoy7RBNv7M7EsIuePYGPPqLxSy8Bf/hDMK9rTjF5sho/9RSJHMmgi04//QSUlipBJQwsW2ZevvVW4C9/MT9nPPQQvXT69QPmzaPxoEFqfceOzvz9WHRKDsPT7v776V550iTykj32WJpEeucdulalsYNRJCE6GwSf4vJyTNi9G0sB7N650/4DDB+uxIUtW+iC4XMyhMDQ+vVRL5ET8+bNamx07WIq44ToVF5OHeZ27TK3NY7k3HPN2RpVkZ5OD236BfTmm1l0skrr1pTtuHs3laetX2+9vNFPFBSocb164X3A4PI6/6OXEv/tb8D11yv/Ikahd23iTCfvWbLEvHzJJf59uNIznebNAx58kDIRGEKfgD3hhOT317IlmVTn5dHkz7Jl9hpg+53ly9X4iy+oA50Vhg5VopOO3uDFTho0AHJygKIiEkkKCrjaoyq2b6eugzNmABs3AkeOqJ/NnGl+r9asIdH1uONI2B07Fhg1CrjrLtfD9hsJi05CiKYAciLXSyk3R9mcsZGDZWX4naGkRyrqdnDjjWq8Zw+9AkCzrCxsGDAAOYZpZDykNF9oWXSKjd2eTitWAL/7nfnCbHDFFSR0zJpFXgx6S1mrtGjh/axNUEUnIYD+/ZW/w/z5wRSduLSOqF+fvkfFxXRTeeAACxp+oqBAnVOzsmg23Mr1K4xEZmEWF9Nnm3GfoiKz599f/0piqV858USVAQ0Ar74KPPJIeCcjdA4cUNfL7GzKFreDAQNUo545cxITnRYtAiZPRrNff6Xfq1kzON/1sjLlPQcA3btb/90bbzRbRBjotid2IgSJg8bER15eYvGGjY8/pvNcIs/EEyZQI4zf/56u9TNnAmedFZyO1g5haXpCCJEmhPirEGI3gHwAG6K8GMYTth85gp91X4x47NkDHD5M4zp1wv1gWhW6p9MPP9BNSjI8+mh0wUkIuhmcPp0yqrZuBfr2TXz/ekmRgdsttoMqOgEkOhkEtcU0m4gTQnC2k5+JbJjAglNsOnZUgumOHcCf/+xtPGFmzhwl4HTsCNxzD9CwobcxxaNJE7O/4/bt7LdioHdwbtXKPiFON5V/9VXr92Dr11Np2f/9H7qOG0efq5wcErHiZcX7gaIi+j4Y98g5OUD79tZ/v21byoaJxMmu2vqE+wZ+hI/JnDlk9REpOPXtS5PosXj0UbJ30Cfsx493JsYAYTXT6VYAfwLwFIDHADwOoBzApRX/PulIdIyJLCFwfuPG2LlrF5ro3ebs4qefVOnZMcck1z7VBabv34+dFabNh4wboaqILK3jGa/YRHZeO/VUykSq7gNSrK5oubnKSyCZz7Vep26wd6+7N8UsOnkLZzopWrSgNHCAzMSdStVnEmfNGjX2qx+OX8jIILPW226j5VdeoRt6fVKEcRYpyXT6gQfUuqB0Wuzencr1P/+clidPNpdshhWnMv4vvZQy1YuL6T7i22+BESOq/r1//UtNCOvMm0dNZuJ1ffOar74CNm1Sy127Jn6fPHYsZXUePAh88gllOY0ebW+cOl270mQyQJFgt+wAACAASURBVJUzI0c6d6wg8+qrqrFWmzbAk08CQ4aoSe7LLgPee8/avsaPB+6+25k4A4JV0ekqAI8AeA4kOn0mpfxZCPEYgO8AsBuzC9TJyMAnPXti6tSpGKrXqtvFG28AzzxD43HjfH8SOmvxYkysUJ8PWe22x6V11mnShE6sRverefPoAeD114Grrkp8f/rfXidahlJ1iGYanZfHopNVjj5ajZ0o33UDFp0U7dqRSAwA339PvhGMP9AznTp39i6OoHDrrcCHH9Kss5QkHFxyiddRhYdJk8yCU3a2ednvnHyyEp1uuokeGp24hw4S+v1Yq1b27bdZM+Daa0lEAqhDcVWi04IF5kYykSxa5O/mJpFNIa68MvF9CKGa51x9dfIxVYVe5hXU+z2nKSkBPvtMLf/vf2ajd4A6oK9cSZ/h66+nhI0//lFlhOrMmkUd70J8L2bV/a8DgJ+klGUASgHUAAApZQlIiBrjTHiMq+hZJrt2eReHRWpqMwmHqxKdysropvWcc9Q67lwXn/R0qmWO5MYbo89IxaOoKLYvlF2iU1FR5XVuejwVFlIdt0HQRKdWrVSnlF276P8TNHTRqX597+LwA3rq9+uvB6IxRGjQS3w406lqhDA/uH73nXexhI3SUuDtt83rHn44WJ5/p51mXtb9S8OKXl5n9wTsxRer8ezZ8bddt44ewo2H9E6dMH3iRCqLzMqidYWF5gk9P3HgAPDvf6vll14ikdzv6KIri05mNm2ijoJZWaqsrmVL4PjjK29bpw59xlevps/BtdeSt9fdd1MXuzlzzCb9I0aQUBVSrIpO+6HMw7cB6KL9LAOAj4u6GcsETXTSOqbELa9buxbo3VvNIhj4vHzQFwwcWLlELdJM1Ar6DU6kyGRXW/sxUbRv/bhO8+CD9LcCSLAL2kxqerr55nNzAHtD6J/LsJffnHWWKpHNzwd+/NHbeBhCSmDaNLXcu7d3sQSJ009X4zffDPWNu2vs3EkZk3r5yIsvAnfe6VlI1aJbN3Ob+tmzqYwpzDiZ9X/MMcoyYfXq+M8TDzyg3ovsbODf/0ZZzZpAr15mE/JE7znd4NAh8+R1VhY1xQkCeqbT8uWqhCzsSAlcdx2wcKF5/YUXxu7SmZlJGctGIsTRRwNPPEGldwMG0KSfMQldXEyit999yhzCqui0EIBhbf8tgIeFEJcIIUYDeALAz3YEI4RIF0IsFEJMqFhuKISYJIRYU/FvA23be4QQa4UQq4QQw+04fugJmuikZTrFLa979tnKBtZNmtAJgama+vUrP7CuXJnYPvQbnEiDRb3NfTL06QO8+655XbSLh1MYnd8AMrsNYvlmu3ZqrHsUBAX9c6obmoaRzEzgjDPUsh9v2sPIqlVK0K1bl9oqM1Vz7LHU6tvg5pvt6arKxObdd83ZwnXrAtdc498yp3iMHasmgkpLVelxWNGvB3bfq+TkkPBkEC3b6cgR4OyzqWTJ4JNPzOJyZBt6vzFzprl5ySmnADVqeBdPIjRurCbmDh9mM3GAsu1uuKFyJm2dOsAdd1R/v0cdRRNNenMXqz5QKYZV0ek5AIcqxmMBbAfwHoAPAGQCuMmmeG4BsEJbvhvAZCllZwCTK5YhhOgO4GIAPQCMAPCCEILbvySLLjr5NZVVo4amOh+Ol+mklzJcfz3Vh2/dyplOidCjh/mku2KF+edFRWSSt3OnWldcTCWNL7+sDAsBusHRS5/0m5NkufRS4IUXzOv0GU4n0btbGKa3QUP/Thgm1EGhqIh8xwyCYnTrJLrAG7T3MxUpLwfuukstDxumMgKY+GRk0LXEoKwMmDvXu3jCQKR587nnqhLsIHLyyWo8dapnYXjO++9Tt2CDbt3sP4bufXPTTeR7NGECZcndeSdleOrdvAYPruwjq/vd+XHSJDImw8cqKHTvrsZ+/Pu6zcsvm68xBk8+aRaMqsNRR1HGk8Gzz1IW1JVXJm5XEmAsXT2klJO08XYhxHEAOgKoCWBFhbdTUgghWgEYCeqMZzyxnQNgaMX4LQBTAdxVsf59KWUxgA1CiLUAjgNQRfEwExe9Rn/VKn8b9yGBTCddwf/zn80nWsY6escXI9OpsJDqlh9+mITKunWBL76gGv0bbqAyiEhatwa+/JLKf5o0sV+gifTqcuvBxCitA/zdRjoeQc50mjePZk8BoEuXyt0Xw4guIgbt/UxFxo2jc5+BPqtvIzNm0Gk1P9+R3VfJgAHUl6ROHZt3/Lvf0TXcMB2eNw8YNcrmgzAA6P5v5ky1PGgQfX6DzNChwD//SeMwi066afdllznjz3XRRcBzz5HQvnkzZQHFolkz8wO5gZ7p5EdRRJ/QfuIJoEMH72KpDu3bq+8B3x8An36qxkcfDTz/PJXU6Z5MyXDKKfRsYExQz5tHr6OOoo6PIaBaUxZSSglgbZUbJsZzAO4EoN+m5Eop8yuOmS+EMEw6WgKYo22XV7GuEkKI6wBcBwC5ubmYmgIXmoMHDzrz/5ASg2vVQkZhIbB/P2Z98gmOJNPC3mH0XKyVGzdiapSZfFFWhpM2boQhnf24eTPKOSW/WtQ9fBj9KsYH58/HsvfeQ+8770QNo7sdQKVy+mxiFNYUF2NrWRnERx9BZmRg21crMXZsD2zaVMumSM+AgGYqvgOQWSRKSnkihChHw4ZHcMstqzFw4J4Y+0iMtOJinFRhZF6emYkf5871tWAbi2aHD8OQFnfMm4cVATpftvzsMxjzovnt22NVgGJ3inp798LoSbh/8WIsrObfxLFrTsjo+7//wcjxLK1VC3NatUKpA3/X22/vg59/blD1hg6Rlwe0bbsKo0bZr3o1rVfvN6+HPd98g8XxHmY9JsjfmzorV+KYHTsA0Gd1xqOPUoZzZJZzgMhMS4Px+Fj+00+Y/t13kIZZdYgYuGYNsivGc848E0UOfUabPPAAjnr2WWTGsVAoy8rC7P/8B6Vbt/5Wyml8b2oXFKB/xXbFM2ZgzuTJkOn+KWrpPWfOb4bGS0tKsCtg3/W2UsLIhd48bRrW6xPLIUOUlGDw9OkwPl2z774bxWVllFFr4/va8dRT0frDD03rSp56CnN79kRpkh2Xg3C9EaQfVbGRECdVtY2UstoupUKIswCcKaW8UQgxFMD/SSnPEkLsk1LW17bbK6VsIIT4N4DZUsp3K9a/BuArKeUn8Y7Tv39/+dNPP1U3TN8wdepUDHWq5eLAgeS2D1Cb3FNPdeY4NvDcli34y7p1AICbW7bEP6K1nt64UZWY5Ob6smxw3TrvPJtzcxNI/Io0FK8uX3xBtfwV3H478Mwzye82UQYOtNHWYetW1Xa4WTPvUgySZepUJRoOGmSe6fY7Y8cCjzxC4wcfpOy7sLNpk8peS+Jz6eg1JyxISZ5ERofF1avN5SM20qNHZRtDt3ngAfV1tJUNG1RGQf36wO7dsQ1ePSaw35sNG4Dhw5WPzplnAhMnehtTFA4dAn7/+wQvU3v2AOUVdgz16wMZ1S9vTUujisOXXgrQHJOU5LlkZAUXFgI1azp3vLVrK5/nevYEli6l8c03mzOvoH1vjhyhzHhjovjjj4ELLnAu1kSQkjqaGdfUpUvN5txB4O23qbwLIKPsDz7wNh4vKC0FPv+cLEAMa46OHelz6wRFRfQ53ry5cnbTgAHAH/+o3pME8dP1RgixQErZP3K91UynqQCqUqeSkZ9PAHC2EOJMUJe8ukKIdwHsEEI0r8hyag7ASFHJA6A737UCddVjkqV7dyU6rVjha9FJL687HKu8Ti+t82Hq6xtvRG+65iYPP0zP6FXSoAHV4S9eXPlno0dT6cMHH9AJPNb7kZFhrvUHiW5eYKv+mAqldYC5HGv9eu/iqA5684NGjbyLw0+0bEkdVcrK6ANfVEQPHIz7bN6sBKcGDYBOnRw7VHGxGk+bVrl3g1O88grw6KM0PnDAoYO0a0dl2Tt3konvN9+QKMLYw5Yt1BrceNDPyPBtt7qPPqI5rMTQrs/7Ym9llZdfBi6+uMoEb/9w4IASnGrWdFZwAug817y5ecJjzhxq8LJmDflwxiIri1rQP/44Lf/jH/4QncrKgJNOUv8nIRw9nzsGez5SyXCk+DNkiGnx66/JkswecgBUNLE6+3fAl9oJbC5wRs9MDKue5hQIrIpO0U6njQCcBWAIkjQSl1LeA+AeANAynS4TQvwNwJUAnqz413h3vgTwXyHEMwBaAOgMYF7kfplqoKe9eD1VWgU1tdnNQ7GMxHXRya077wR46y2vI6AbN0uiE0B196edpkSWgQOBp59WQtKFF9L044oV9LTTuzdtb9Cvn9mwHubmOFOn0v2uLVx2GfDxRzR+5VXg8svx5ZezcOGFFKtdTfMAmE3EG3hX1pI0bdrQjd6RIyRS7N8PJJny6xq7d6uxj8uCXSUjgzLwDL+GzZvNPhleYsywByZFIEl0sb53b0f/37ro1K6de4009ePYen7VEYKe8g1vnocfBkaM8G22U+B4+GElOGVlUXexiIcwv6A3xfWSuXMDJDrpzV6aNHHnmCNHAq++SuNGjYBatcg43Eqzj+uvJyPnsjLqTjt9OnDiic7GWxWzZ5vT5Nu1A7KzY27uW3QPz7CKTm+/XXndWWf9Nly40Mk5jaMAmLviNdwyFcOcOpwPsGokPi3Gjz4VQjwLYBSAr22LSvEkgA+FEFcD2AxgdEU8y4QQHwJYDqAUwJ+klHHalzGWCZLoZMVI3Oeik+HHDZBXnVuNjA4dUo2+iorib2vimGOABQuo+8mAAXSnFfnwVLMmbWd0pdOzo666qtIuddHJ1mt3p9YAKmb08tYB2UC9eqrnQUGBjV75uugU5Eyn9HQSJYzU91WrgtPSXc90YtFJ0a6dEp3Wr/eH6PTppzTD3bkz3cDXssvPzcfMn6/GvXs7eij9nO5mYptuHO5YphNAHQBffpnUtXnzSBz55z+Ba67hboDJMHu2ufnHhx8C55zjWThVoScYP/ggNUmrkl9+AU7TMvh79yEriQRvBN57D/jLX2i8YEFCv+otXohO991HWfAHDlTuLlwVrVsDV1xBZQEACVBei06R9ZzxsrX8TIsWNDFVWkpC86FDzme++Yk1a8xm8MccQyKnds5buNDlmPodXfU2AcaO3qcTAbwP4EYb9gUp5VRQOR+klLsBRHWJlFI+Dup0x9iJniLq824GNbSZzcOxMp109V5X9X3A3r1AhU8ncnIoMcgtj0TdFqMk0d6T7dsD99xjffsnn6SbhqOPriQ6lZSoMjchku9KWilOg4rPQVaW/C2Rp6SEnllseShLFdEJoC6Fhui0YgWLTkGnSxc6uQAk/o4Y4W08AN3YFRUBS5YA//63b8t3bKG0lAQSvfOXw6KTnunk5gR83bpq7FimE0Blo7fdRh2jAMqCuPFGSh2eOJHLa6vD6tVkp2DcSw0Z4mvBCaDqSoNWrSxqKMN6Aaf2Bb7/npYXTwa2LqL7kwQ4SXO6ZdGpCtq1oyzbffuqdx9+zz1KdJo8mW7gvDSA10Wn225zyLzOBdLTKbvdsFLYuDFc3b0nTFDjs8+OWqtbWqrGxx1HLiJOcuKJAaksqCZ2iE5dAMTpV88ECv2pPz/fxlQQ+7GU6aSn0bhVY2ARXWDv3Nk9wQkwX6+N8n7HOOMMmkWJ8jnavp0+YgDQtKnN9xG66KSZAtatq/SJ/fttEp1SxdMJALp1U2M9Fc/vsOgUnX791Pjnn6u9m/x8mqROxAstPZ2MdivpXPp7NXFiaotOTz5JrtoGDRrQH8VBUj7TCaAysKlTKTvHYO5cEk5mzQJq1HA4gBTjX/+iTAeALpLPP+9tPBbQL7uWq9rT0oBvvyXVyBAP3nwzYdGpZ09Vib5hA9kCuHmrXLMmnTaHD0/wF70QnQAyba9fv+rtotG5M82Srl9PivrChZRp7wVSmkvrrrnGt89IlujYUYlOq1eHS3SaNEmNtZI6HV106tcPuOOOqJsxFrEkOgkhroiyOgtATwBXA/jUzqAYD6ldm14HD9LVdN8+33rUWPJ02qb5y7do4XBEiaGLTm53KtUrEBLOdKoOMS7KuibYsqXNx9QvnsuW/aZu6aJTQQF18EuaBD2dSkoczgSIQ506VYh7+ocxKKKTlGwkHgubRKfbbqOq2kR57TV6KDOaO/5mpm2wdq2vJzeSJrKF8fjxjoqi5eXmc7qbCQF6ppPjolNmJokH775LPi/vv0+fo0WLgPPOAx56yEaDwGCxdi0189Svr3GR5cCP5wM4n5Zb9wBusS5ING4M3H8/0LdvwqEmhS46JaRnpKXRH+j002n5o4+A555L6ByUlQX06qWynObOTeD4NjFlCvlqJ5Q8NK8ngL/ReFl/4P+qd+zBgykRzrXT9qBBShyZNcs70Wn1auUf2bAhZRIHmR49lPiydKnjEyK+obzcPGExLLqTkv5omWFHmk7IsfonfDPG+mIAHwC4xZZoGH/QvLlqlZuf71vRyVReF5npdPAgPe3oyk4MVaO8nJpizJjhRJSx0Z/n3RadXM10ikNenhr/9lBqFy1a0J3ovn30qhAgHSkBSaC8bu5cYNQo84SjmzRoQN6wMWdI9Q/jihWuxJQ0hw6pmqKcnHD5ElRFr17Kt2HNGvrQ618Ci8yrZquO0lK6l20lttL38KefzBts20Zx+cFrygn09pxTp5J5n4Po5/OsLHe1PD3TyRVRvU4dajH9xz/Sw99DD9H6b7+l1+mnUwptkybAY4+F5rxw//2Jdj9PAzBULS5L/Ji//ko+z26il9clfJs6bBidBwsK6D5X9xywyA03UHM1L/nkk0R/47iKF4AFFa9q8Pe/0/vtmr3SoEEkMAP03TYMtdxGL60bNCj4DQx69FBjw1YhDKxcqU4gTZrE/O7rmU4sOiWP1T9hNAfmIinlDjuDYXxCpOjk03TLuOV1Tz6p2qwC9CAa467Ey+uXQcpnOkVQVkaTK/qNse2ZTkJQDryhJi5ZAuTkOCM6JVBe9+KL3glOAIX64otxRCd95m7dOvqA+N2cN7K0LlWzZqpDTg7dWP7yCy0vWmQ2JLGAlOayun/9q+oMmldeUd7Z+y+/Cdj179gbjx8P3H57QjEFguJi8jIB6DPpQuaNV6V1gMuZTpHcfDNd8/UL2nffqfHBg+StFQK8eHZcssT9Y1arvM4gPZ267377LS3PnJmw6HTNNdScV0+od4PVq0ns8uLeTWfBApdFJ4NvvwUuv5yE5LZtXQqgAl10cngCwRV69lTjMIlOepbToEEx7xlZdLIXq93r/O0ozdhLs2ZqnJ/vXRxVELe87vEIj/kWLWKeVLyuIKpbl25c3MTrTKfXXiMvYR3bRSeAHrYN0WnpUqB/f88znfQ2z3XquKfnlJSoB8G4D4S1apG55ObNdMVdt859VTRR2M8pPv36KdFpwYKERafDh9N/s3upUYM8m6vS9ebPV6LTvl1VPB199llqik4bN1IqLUDfKRdcvb0yEQcqZzq5WjXZsCHwhz+Q2hmNV14BLryQPJ9SHP227bPP4pSelZUB558PFFSUvGZmAf/9b0Ln0OHDlRNDYaG7jSirXV5ncMIJZtHp8ssT3kXbtu7rHgMH0nPyxInmh2JLPPKwugG46iqge4/420cwcaKqGNbPNY7Tuzdlp02ZQsvvvks373pXUCeRkhoXvP66WpcKopOeVLBqlfcm7W6h+3INHBhzMxad7CXhP6EQoimASvNnUsrNtkTEeE+kmbhP0TOdKpXXRRJH0dAvnOecA1x2WbKRWSctjWrj3fRzBMwnz7Iy9y1VoqXh6xNZthE5i9O/P+ppzSEcEZ2qmHLVM0amTwf69LEphiqYPl1pDVUKjV27qgyNFStYdAo6/fqp7j/V8HXavVvdhDZrZu1cUU/uA0BPgvtQxRPhrFl0rbG1faUP0BoYmDrDOoiXolN2tjJXLi21sTuoVZ59lhpIZGfT5/zQIVJdDK67js5nbv9hXKS4WF2O0tKolDtmk5InxgEFX9K4RQuaEUmwXKhlS6pMA8hDyq0q2bIy8/W7XnWaPumiwfTpScfkJp07A7femuAv/fWvwIGH1PJ1vwMSTL7ct88j0UkI4NNPqcuYcQP500/04WsfrRjHZt56C7jvPrWcmQn07+/8cZ2mbl1STTdtopP2qlVUkp/q6JlOLDq5hlUj8boAngdwEYBYV2sXe28xjhIQ0alGRKaTlBJCiOhTP3FEJ/0BvFcv51ti+gEh6JpppGeXlLg7uaH/zZs1o0Y5Q4c6cCC9Xr3Cn8iRTKcETKz1r5Sbz9gJlVR27apKU7xOBbQCi07xSdJMfM8edXKw+pmtv3wmgJEAKkSnyy4D/vMf4OqryfQ5PZ06im7cSKr3F1+QSUqqUFxs/v907OjKYb0srwMo28nw2S0ocDmGWrWovbpOfj49oRcW0gNqTg5lTDz2WNyHjaCiT2rk5sYRnL76Crj3XrV8wQXV8qdp1UqJTnl57olOej+CevWq2f13wAClki5fTlm9Ln1PXWf9euDBB9VyrVrVss7Q9VpXRSeA3ugffqD7OuO+ZOJE4KabnD1uYSFwS4R18WmnpU6HzD59SHQCyLwx1UWnPXuUX2lGRlzxkEUne7F6hfk3SHB6DcAfAYyJ8mJShYCITplpaciomHIvA1BS0Z0saoF9nGkwL2eGvUQXIdwusdP/5i+9RFUPjmRa6XfAFYa+johOv/6qxk2bxtysuFiVBKSnu6uPJFRS2a2bGgdBdNKftFh0qkyfPuoLtnIl3UQnQGSmU1z27gVuvRX1Z33126r9Z1wMvPMOGTm/+SaVKMyYYS6p0zNSUoG77jJ3SghBphNgLrFz3dcpGs2bU295nSlTKMtl7FizG3UKoJ8K4wrEL7ygxk2aVBbrLKLP5+kfd6dJys/JoFYts7nhiBG+vudNiqeeUq246tSh7NJqNJTwVHQCSBj985/V8vjxzh9zzhzzzeIzzyhT81RAF9/1DKBURW812bdv3AYTLDrZi1XRaTiAO6SUN0spX5ZSvhX5cjJIxmUCIjoBZl+n30rsNm6svKEx9RoFr2/SvcJLM3H9b+5ohlXz5mqqffduZBw8aL/oVFgIHD5M4+xsoHbtmJtGzkK72fgkIdEpaB3s9O98Qv2jQ0KtWuo9LS8HFi9O6Nf1TKe4otOaNcAxxwDPP496UKkI++q1U9tkZ5OXyPHHm9szT5lifpIMMkVFJK7p6NlmDuL19cwRUT9Z7rsPePRRc4tUKYFHHiHF4p13vIvNZixl0h46BEyerJYnTap22q3+J926tVq7qBZJ+zkZXHihGq9dS+ckYwIzlTC8qwAS+Hv3rtZuPBedAGDkSDWeMsX5ziy6EHP99dR5yKddvauF7m2hex2lKpEm4nFg0clerP4JBYBVVW7FOEpJCU0OL1pU35FrYvv2Fc9r+s2H2205EqRmejoKKmZvDpWVoV5GhkoT1YljEOn1TbpXeGkmrh/P0b95Whp1pFm+HABQf8EC1G101m8/tuWhSL/hado0bsqW5VloB0i4vM5g5Ur3Tb8ShUWnqunVSwmIq1cnVFq0d69F0enqq3+rtakPlUGyb3+Mz06rVsBxx1FKf2kplUq4aarnFOPHm+t//v53KulyAT+U1xn4ItMJoLTS++8n8WnlShI99dnuW26hB9kqmkAEAV10ivpdXbnSnMnavXtSxoK66ORmppOeoJbU8//ZZ9OH1viwzptH6pn+Hws6Upo/GEl00fSF6NSmDYkFs2bRdeOss8gOoFrGXlWwfTvwwANqOQVLctG/PykqpaV0j7B3b2qJajrl5VTKb1DF+6n3qKpWCS9jwqro9D6AUQC+dzAWpgr2bi3FtGGrAGRiGpbZvv9poBL3Ns1LAYyllevSgNFLffvAedvuUhRWnBQ2N1qFPenpwPIc/BY/QGUN73QA3o3+N+v/k9q63VvAshmOhuwbbjsAGM8nW64BdrlYnn7eYsDon5X+GLDsJXv2m9MuB63vaI2sJpqi1qnTb6JTz4cews8XHQvgTAA2iU56aV0VjvBVPhA4SEIiY24uTR/v20c34/n5ZDTrVwxTESCm6HTg/9m77/goqvWP45+TRkKoomCkimADCwooYsEKYsPerr1d67XrRX+2q1577/XaKxcVRa81CjawoCIgRZp0CJ2QhGR+f5xdZjbZTbbMtuT75pUXs7OzOyeTnZ2ZZ57znNXuAG6p1rlz6kc4Ciorg0cfhZmTr8ImLQMP7Gi/8KM0Zoz7uY74uf3tt5BivG0uPQMesNP19mAaNsxe6IE9GWwMQad33nGnb7wRLr88ZatO902UjMx0CjLGBlw+/hiuugqeesrOX77cjmp32WV2NLdUDsHms3oznRwHzj47dN5hhyW0Pm/3ugkT4KOPEnq7qHlHrk/o+rhVK7u/7r+/O2/cuMYVdFqxwj3ot2yZ0Oc7I4JOAKed5mbljBsHxx7r1qH0y4oV9qaIx6h1+/PCMf7eqO3XD669NnUjGdfRvLntZvbDD/bxd9/BwQenqTFJ9s47bqZ3UVGDo5kq08lf0W7Cj4EHjDEtgdFAWe0FHMf53M+GSV1OlcMgkpxG+j2BNQyyj2uAt5dGXDzd+nmmyynDdnLajI3tB5gOTI/8O3QN/ADwEyyJvc5uVvLe61rzPqxJ4bq9ZQqdL/D1U/35wjJ+ud5zx2vYsJBAxJ+5f8IlUwEYuyVcNDXBFa5c6dYY6NLFZpFEMLEGCCw6c3sf1h2DNWvddS8ujmLdV1/tpmZNmQJrUvkJidGQIe5FQ0FBnb/B6jXw2qup70a6kbGJLtulYRDAr7+BCUuBfVvBvnt6nonhw3ekO/luV/gl3Eu/+MLdD3r0YFnfHaDGLjhlk3o+b/vt574uP9/eac32W4o9e7q/0/771/ud4LdZnu+YGV1T+x0DMO1goKedfsjARylef9SuuAKOPBI+/NCd98MP9nvOG4BIZ8UJHAAAIABJREFUwDzg7ST97Qtzcjhl883ZKdCde8YMW0Vg4kR3mTpBp3feCY3WdOwYWh8nDt7YzDffpOdaNaHudWC/g666Cu6+2z4eP94GHxsLb4p1gne7MibodNxx9hwlmFH6ySf2BmA9NTVjdvPNdkTHgBWbdOf4KzpurKbgl1GjbP36k07y931jsscebtApXTtysi1dGloQ/qKLGqwBqqCTv6LdhMFctC2B0z3zHWzXOweNXpd01dXp/IYXyQ7lE9by6HxPoeQttwwzpK7tNjoLeDTRHqT5+fbiZeNb1/OGm7DxAn4SMCnVvVcD615HFL937bTjTO5qe6jbZTJi/bZDw89Olc+Bz9OxCbsFfnzyAQR3n1A77hhaJ6Ri/sbP23Ia+Lx5959FixJuY9p5h+Ksrk7tvpPPxu0+Bx++32K1AxvvKnwCfJLBXxsUFoZ+9oL8/Hsl8W//2uLFzNp9d267JYebb677fEjQqarKFrcPOuMMePbZhLPYt97abkZvt85U8/YWjFs/zy3MYOZlY9EYg05t2tjMpt12c+d99ZV/w0+Xl8Pjj4fMmnnZQ5T/X3J6fQSS8dNnwAB46CE73ViLiV9+udsHuG1bG2hugIJO/op2E+6b1FZIVPJblXMzYc4smpg2bdpw55130qZNG15euIhR9RQJl6ah7XK45GE7vVmSkwFFRETSbX5lJQsrqnj44fD9KLfdFns1e845oQWCW7e2WT0+lE3YZBN45RVbNz/VtSEBeveGc8/14Y283ajGj7cRlcZS5LMxBp3A/s2uuw5uu80+HjnSv6DT7NnuL9i5M8yezYpSd3/p3dtdbbzefx+eftpOe0sApoW3oPZnn9md+uST09cev1VWwn//6z5+4YUGy2GAgk5+i2oTOo4TQ/UHSZacZjmUUpruZqTfChh38zjatWtHTW4uq3v1oipYQDBQMNEA2xnDEfffj2mgq8bjT8CkQLmn8/4OvXslt/mZ4o47YV4g6H/1NdA5hSUMbrwJygKxwptugnbtEnu/nLU18PCfAHQog4e32so9oa6qgttvt6m1wHqa8X/N7mJ9RQqHjgvjmmuhU8eGl/NLZRVcESgtk59vR/2t12+/uTVPttnGpiJnop9/huees9M77BD2CuR//7MneAAHHgSHJ1bGJGozZ8F999rprl3hyitTs16ve++DWYGSV5eb+9nSsfsJ998f9VnUtOnT6dmjR/gnly2zOzHYD9add0J+Pg7wj0vcgaAeeBByI+1yCxe6Z/B5eXZ/LUphkTk/jRkDb75pp/v1g1NPTenqv/vOXi8A9N8NTklxiayffobnn0vtOjPJsGFu77x695sE3Dx7NksDfYV/m1pNWaDgRWGh/QrMzbW9j7bbDjjq+rojUl13XeIHXY+jjmoEvdG6dLFf0rNn2wKA778PRx+d7lb5o7EGnQD22cc9drz6qh0l9IorEn9fb1X8LbcEY0JGTOze3dagT8Ty5W7Qqd66h6nQubOt2xnMzPzb32yB8W22SW+7/PL113akabB/vChr2Sno5C9twixSUFDAMcccw5IlS9gsighttJYts+U4wN612m8/bD/m4OguJSUwcKBv64vXhg0beCdQoHX+/PnMD345TpgQdvn5wJXnnsuQIUPqfd93voNJgdGDj74ADmxE9SPr89I4mBfIIj/yGtg9hb/37aOBQMHTsx8JLUYarzGtZ1O9spqcSji3sENoMfGHH7YFAwP1LA48oBMjdruLqmofAk/vvw8Tf7PTQ4fCjg2PBLTffnDgbg0u5quqKrgiUN+4JhcuerOBF6xb5xZE3mILuOOOpLYvbi+95LazS5ewBWAXz4b3A4sM3BkuStFnfcJSuC+w3tY7wkUPpGa9Xs9+AQS+Iv+2+WT6LAzUsLnjjqhH+iudPp1BkQrrfvihu/0PPjikK+uNpe7Q5ic/XU/5hE6d7DHnxx/t45NOCt/tKRt88om7PfbeGzp1ompZFRXzUnOFVjwBvg2sflAunD40Javd6NROcMge8EcU4x1XtCigqnlBwwtGafFiWx8lruRnx4Ga6vDP5eZiK0nU89IaOz1vHlx0mp2ud79JwBPz528MOn39Q83G+UOHwogRngVramzWgtfAgQnXcWqUjLEB4n/9yz5+/nkFncLIuKDTHntAixZuzckbbrB1exKNEMyb504HTlC9QSc/Bnfz1iJLe6ZT8PPvPc/7+OPGE3T63//c6cGDo36Zgk7+inoTGmNOA04EugC1B+J1HMfZys+GSV0tW7bkrbfeorS0lEHemhEJ+vFHG9AGew3y1lvY7IFddrEzW7UKzEy/q6++mruDxR6jMHbs2AaDTuke7SddvCNlpLrAsjcN369t3qxjM9atXAdAxbyK0KBTYaGN9ASCTjt9ei879Vhbp89+zMrL4Q7PHZO/94bD4h9+Opm8B8zqans9klNfzK17d7dYx/z5/hfp9MuUKe70tuErdadrH/cOWZ+umife9RZ23gyC1x9jxvhzQukdMahWAeY2bdwT9S+/bGBAqG7Hw4+BrNSvKsCHwRJ7907xQGTTpoUOx9y3L0vfXcrEoydChHiG37YEng0+GAE/jKhn4SRpB+zR4FJg8gzbv7E9mx3l3020uFXXwCGHhl6cBP333XrTGpYvtwN+VlXZnlnBJL2amr3q/46NU8UDuRDYde+43w067b23Z6HFi23WjncIwSOPtNmrTelEJxann+4GnUaPtsOd7pSZx/OYNOagU3GxjbQGAwnr1tmBG7bfPrH39WY6BQ5c3mykhIvXY3u5BqU90wng1lvtSMBvvGEff/klXHxxetvkF++AEQo6pU1Um9AY83/AzcBE7D3TTPiqEZ+EHUq9Z0935owZ9mwqbeN5uu68804uuOACVkUai/nJJ/nssccIDlA9LoqCkN4DZ4F/N10zzqeffspHH31ETeCW7OzZ7nMPPmi7w4fTokULTj31VHr42E0gGdu8WcdmrJtkg06V8yph51oLbFUrLj5yZOJBp7//PfRxJgZlAoyx2zq4j1dVNXDtkZcHffq4RSXHj4dDDkl6O2PmTamIEHQKCbzUvmWSRJkWdGq27x4w/kX74L774MwzG4g8NmDuXHj7bffxQQeFPO09MW+41MZVgR/ggcBPglq1sr1Eu3RJ/L3qtXixvWD1ntjm50Pfvvw19I+UBZyyjbPBYcHTCzIj6JSbCx99ZLtgjBsXSPkO+Ne/7HdfhK76bdvaj/4HH9jH7j6XpPF11rv7bE2e++HaGHR66ik477zQ1xx7rNvtU8Lr3t0OSvH++zZ97eqrwwchs4nj2PS/oMYWdAK78x1yiLsDPv+8zdhJZBTUMEGnZGY6ZUTQKTfXdr0NBp1GjLC/tB+/bDpNmwa//mqnmzUL/W5vgIJO/op2E54FPOg4zmXJbIykR9igU4sW9kx9zhy7102f7tMQIYkxxtCtvi4hK1fSCjYGncaPH09NTQ059VxYNYVMp7Fjx3LQQQfhBAus1DKigbvhb731FpMmTcL4UHgUkpfpFFTxV5izIW8gFfwZJStYKCgow1OR8/PdbV9ZGcW279/fDTqNG5d5QSfHCc10irD907WPZ8IJune9haefAI9dabsiTJ5s+1XHOzx8aSns6xljZO+9bUEZjx49bNJsuqxaBe+9l4JyZLfcEhpwAth1V2pymrHqO/cGSXHv4vp6afli0WL3q619e9i8Q3LXF4+aihrKp9pxx9fPSeOwZ+EUF9vP9fz5NvW7stIOJX7fffWOdnTLLTZr3JtUkjTrPRfThTUYA6ecAjv3qoKvvg0dFjwoJA1KIrrjDpvlVFNjszinTIl4MyMrnH9+aLSkMQadAHbe2Q063XOPvVnuLRwdrYoKeyPliSfceWEynfyIw3gzndLevS6oVy9bZyVYKG6bbex1YCrv1vntP/9xp4cMgZYto35pteeGUSIxTLGiDTq1A0Y1uJRkpbBBJ7DpqXPm2Onff8+IoFODfvuNrsCmwFJgxYoVFBYW1hss8XYt2203XwZ0yTgbNmyIGHCKxpQpU5g2bRpbb711wm1xnNDPmV+ZTgUd3TdaO2kt5TPLQxdovxOrtt2XVlMmu/MmLoPi5vGtcNVqKCsAAidxr70Gy5vB8vJ6X5ZOnXIheG6zdjrkNZQi3nV3IHDXq/QPqL1N023JElhZBBRBcQtY3yZsG/OXbvwrUbwKymempnlmpbvelutSt16vVmshuOfnrC+g/MjzbR0sgGc+gO5RdIRaQN396dI7cX874NRr6mz7G86123tBNBfia9bCjGl2uqi5HYs9TvPnwRI7bgDV81Kw3T+bRMi2ANhqT9Z8sIya9TaztKhHEf1+61f3tT677jpbhx3gXxfDYdcnfZUxqyqr4ut2tqtzxZwKHMfx7YaGb0pKYPhwt0j+DTfYmicdwkfxdtnFloHxXoh/9dVX7J2EYM8JU3N4LxBHeHVEDUdtGggGXHM93HVX3Re0a+ffqF6NXa9ecMQRbur3K6+4Xe6yzYYNdpSuoPz8kJp78cjooJPXyJG2/EEsA1JUVdmsqa++Cp0fpqaTH93rMi7TCWzm8zHHuIPILFli6/vus0962xWvF190D4gQ8/egMp38Fe0m/BLYCfg8iW2RNKk36PTRR3Z60qSUtikuFRUwaRIG2A0I3POgKoaCRamubZRqLVq04MYbbyQnJ4fnn4eJE+38006DHXesu/zLL7/Mz4FUhTFjxvgSdPJu4/x8/4J83kyneQ/NY95D88IsdUPowx1+S3Ctr7mTJwJ8n+D7JZfn3h1TdonmFZuz8Xf8Cuieib9foH1rga3Cd6c9OvADwLXw/bUpaFbAxk/Iavi+e+rWG1T3bz408AO8Drwe3d/0+zqf7WtCH55tl6rttKjevZbyCvjFp8/aHfB90mvg31B31ivAK79vfNh6n9Z1l0mCdHUljUVe2zxyinOoWVtD9ZpqNqzYQH7b9Hffr2P4cHvx+ssvdsN26WKznmpl9AXl5IRe4zZrVpOUQRhb5LuZ2xtyq20gwHHg2WfrLjx4MNx7b8IZLk3KKae4QaeXX7ZpbJkWFI3GtGmhXwivv55wik7GBp3C1d6aMcMW9ovWY4/VDThB0jKdWrVyp1evjqLOZqo8/LAN1gQ/O3/8kZ1BpxkzQkcz3mKLmIccVNDJX9FuwkuB/xpjlgGjgbLaCziOU1PnVZIVIgadevVyp7Mh6DRx4sZviOEdO/JTTQ0LFixIc6MyR4sWLXjmmWc4/vjjAXvzIhh0GjIETjih7ms2bNiwMeg0YsQISkpKEm7HunXudG6uzWT3Q86KHArrjHEgIpJ+bfdLTV2MbOguboyhsEsh6yYHBn6YU5GZQaf8fJvhFBzFrLISBg2yFzN+pDrEqbmnn0d5cNi8qVPrDtv33ntRDw0uHkOH2r/vihUwa5YdIblPn3S3Knbevs1Dh8JRRyX8lhkbdNpqK9sN0tvdfurU6INOM2bA//1f+OcCAVu/azrl5tqeXqtX25jxqlVp/VpxFRTYgPsNgZsp0QxFmmkcx/arD35Iu3e35TC8kb4oKOjkr2g34dTA/89HeN6J4b0kw9Sb6RT0++9kvAkTNk7uMWAAf73xRlRZTlts4XZfnjsXNsuAmqbJkJeXR67nZDXi391jr7322jj94Ycf8mHtuiUJWr/e3zJBF3Mxu7M7xufCKbm5uTRv3jy0C0hFJVQE7gQVFESVVlBYWEhx81QOpxVq7l/uQbRTJ8iP5lt78SI3Uthu05j6wyfd0iXuUMltNwktkuCxcJHNtAfbO6Z5ErIPIpk1y+3e1q1ram+YOw7M8gwYsGW3wMTSpbBmtZ2uZ7sFrV+/nkLv53vNavseAM2bQ3ufCgfNnQvVng9oXnyBiBUr3QuE1q1hk2TGe1atgrLAxX5xi7oHEANtBrVhs+NSc2DJhkwngGZdmm0MOq2fs54WO7VIc4siGDbMdt0Jnl+UldnRfM85J21NKvKkQ5RXVcHnn9u7SF6nnJJ5NfiyRbNmcPDBtss82FpB2Rh08pwT+9X+jA065eTY0dZ22MEO7ACxBUvOPNNGf8Aee2pqbF23QYM2FvPxu3td8H2Cq125MkOCThBaxywbg04jRrg9dYyxxdHjKBGjoJO/ot2Et+CeN0sjEzH44N1Bp061FdUyuZKa965Onz7k5OTQLIpbvd7fuWXLzL077DfvYISRYnO77ror7dq1Y1ntO6gZ6uHAP99VA6vreb4y8NOQVbao+8CBA/1pV4zO2M69ETjp4yiPwffc4xbQPeTU0BoR6bad5xd6bwzsuXvYxfbf316XAXzyMux+QIraBxzUyj2pXPFLg/EdX61eDXsEbuy1aAGrg7WNnnzSHXnxwOPc0WoiKC0tZfdBnm170UXw6KN2evitcN0R/jR44BXwzTd2+vgr7GcvDvfdB1dcYacvPQPuv9+f5oV1/PHuqGC3PQD/SG9mSTZkOgEUdnEjYhVzMunqtZacHFskfo897HDiYIsMn3122rpcNfcEnda99hr885+hC9x5px15TeJ3yCGhQafrM7A4WkO8QafaNY/ilLFBJ7AjJ9x4I1x4oX08dWr9ywctWeJ2q8vLs10rS0psEMsTuPW7ex3Y84G5c93379rVn/dNmHdQlmwLOjlO6Hfi+edD375xvZWCTv6KahM6jnNTktshaRQx6NS6tU0LWLTIPjF3rh3NJVN5uwCG698dQbacpPstmkyngoIC3nrrLR599FHWefvFJWDdOnssB3sn3jsAVrKVlZWxybJldjRGsCnZ9dSpWr9+PWPGjGGD98iToGeffTZtQSdvoDHS37yOPfd0p8eM8bU9CSkrcwNOeXmw664RF03nPl5Y6Aad1q9PbdApYtaL9wRs/PjY39ibVbFLVMXBotO3rxt0euABOOusuO5OpuzCqLLSvZsKqf0yiyBbjmfNuriNW/bhMnIKM6GYST0u/tBGMh0HfgL2vxdOPrn+10yBBTP87+K/5dJ1DA0kGrb5toYFwRptQSv2gGdVWiAha/qDOcT+vb8Dzh8V94VrWtTUwDdt2Fi/b1pPXz4TjoP7aauGeU8noQ5RIvvNjG3Y2ML/LIZPzrVdC3fbzV2mtNQeZ/bbD3bfPRBUCbymY1f4pSP8ArAvvL0OsOe+/ZdC8Eyw6l1Y4MP3637roUtgesWrsOCHyMsuW2bvD6VkpDunHbaQJzAd6D6PkiFtueaR5plRd6o+f/3lnuMXF8Ott8b9Vgo6+SvhTWiM2Qc4zXGcM31oj6RBbq79qa62x6mQhKaePd3xl6dNy+ygU/B2AUQ9QofjhGb5+DWSWjaIJtMJYN9992VfHy+m/vjDzdzt0sW/mk7RKC0tZdDUqXDeeXbG3nvDc8/V+5oFCxYwatSo0KCb49gRbYL51lddZftpRrB06VJuu+02AN58802We/O0U8i7i/zjH1HeraupsXf0Hcfe6T/ssKiOvsYYDj74YM5JVjeUcZ6i4TvvXO8oNenscuRdn7cdqeBdX0gAYocd7JddZaX9m952G/ztb9HdZl2yxI4ND/ZqY/fw2WVxue46uz+uWWMPRJ9/HlfQKZqAesLWrLGjXK1aZR936xaxwHQqZUv3Om+mU9kHZZR9UKdUaAa60p38Avii4QyAP/A/S2Ab4KqNj/bgD2qNQPnvDZCE9TY9nr/3E5B92/QCd3L4csCf846rPNPTzo24WELi329yCWnhPOBp4Gnv+5UAR8OfwDPB+YHXzAbODr/uyzzTsy6Ms3m1HOmZdu5q+BO2vz+rjd3MaVQ/Dp/3788Bp8c54nOqfPedO92/f0JpaQo6+SuuTWiM6QGcCpwCdMWGgRV0ymIFBW7Nk8pKz/Vbz54wdqydnjYNDjwwLe1rkOPY6HZQYLSJhngvSPLzM2TkiBRJyYVZGN478WkJ8nlH8VnY8FjuJSUlnHturTOrn36Cyy+30y1a2NFt6rnCcxyH119/nRkzZrB27VreeeedeFruq2C2Wczefz/qRUeOHEmPHj18DVpu9Msv7nT//vUumu5Mp6B0Bp1CPp4FBTZQFwzcXX893HGHvTsYYUj4jT780H7fAgwY4F8/A7DdI665xi3oOm1aXG+Tkkyn++93+2yCDUBlwAhX2ZLp1LJ/SzCocIOISBbIBZZ+uhwyPej07bfu9IABCb1VdbU7ncnVZbJF1EEnY0xr4HhssCn4V/wFuIOQccMlG3mDThUVtYJOQXFeAKREWZl7hdWyZdQjFGTLCXoyRJvp5DdvgCst29wbdJo3L773ePVVd/rIIxtMKTDGcPnll3PhhT7dHssi999/P1vUkwUWj06dOlEcTJ+G0KKXYSjoFOYjeumlcOqp7q28NWvg00/r7zL0669w+unu46FDIy4aN+/fMs5jTkoC6t67qRC6XbBJkN77IKlS5kkYyuRjWvG2xfQa0Yuy0WU4NVkUeZo+PXRY9WOOiXi+sXDBQjYv2Tzsc4mYuq6csYE+Nj3/msteixZDh/Y227ZjR9/X16RNm+Z2K2/Z0v69MyC43KD//c89v9lzz3rLCMTq1VfdY8uJJ9abZByX+vabVavg7bf9XV+sunWzPfP88O03MHlKw8t5bbFFiurar1kDX5aygD5sjo24mLJMK+RVS1UVvPKK+zjBTGxlOvmr3k1ojMkBhmADTYcDhcB84FHgQuBSx3G+ivwOki0inqRnS9DJe3bfuXPUL2vKQSfv3zyVQae0b/Pu3d3peArkz5kDzzzjPj7ppKhedv7559O/f3/mevu4pdhNN9m4AdjRcKOuLfrII25Wx3nnweDB9S6+bNmyjd3qRo0axahRo+JrcARFRUV8sd12bKzSsNVW9S6fKd3rUl141bu+Or/3iSfakXmuvhpeftnOmzCh/qDTBRe4WU6QnOHYfTjmpCTTac4cd/o//wnZmT76yA56lu5Cu5ncvQ5gsyM3Y7Mjs2y42PmtoKPnc//23fb8I0ywZ2HpQrYdVH9APB4/T5nC3Qtt0On4z6dxTr9+ydkXBcq7QsfTbBR5NXDJYb4V5U6aigpo+08gcCf5lYtsLQOfPP+xe8p94b/9L35d337z7bdwdyDo1LmzrREd0dixMPqD2FZ+xZXQrl3Ep9u1g+OO82+UufEvwd2nxvaa0ldhn338WX+DPlrKPQcvYnPsjcOc5SnsFhGP0093Ry+E0FpecVDQyV8RN6Ex5h7gZKA9sB4YCbwAfAq0Ai5KRQMlNbI+6OS9kI+yax1kQFevNIqrqLQP0r7NN93UZjstXGijES++CKedFl3fSsexoxYFKzlutZUdGi0Kxhj69u1L3zQWI33mGTfo1L9/DCNq//qrG3TaZBOb3dWAF154gbHBrrk+Ky8v54EpU9wU2x496l0+nYFO7/oypqZTUEmJjY4Eg07eLou1LV3qFvkGWwcqGTWMvH/LmTPtWV+MZ3spyXTyBp1qZXy9/HL6A05Qb5k5idcWW9jP/W+/ufP++1+4+OLUrN9xKHrsMTjqKADK27aFIUNSs+6mqKjIHuOD6TXffpv5Qadvv3W7LvTo4WvACdI7gp13IOVeveoO3BjimxwYfUf0b962Ldx5K6SwG9WJJ9pTz+B5WX2MsWVIUxZwAujTh0rcwqu5KzLgwBbJnDmhvRDOOMN22U+Agk7+qm8TXo7tbT8aON1xnI27ujEmi3KhJRoRT9K9FwB//mmfzMToTBz1nCADsm7SqMl2rwN70RCs53TmmbarzJNPNvy6Tz6xP2DPFF54IXRDZri4L8a9J62zZ0f1kscff5wLL7yQhVHUzYpWdXU1M2bMAODTdeuoAXJychoc4CBTMp0yqntdkHekz08+sRkT/frZukpTprgZHO+9F1rLafjwpLSZ4mJ7YT9/vj3jGzYM3n03pmzEpF8UrVzpFhAvLLSBbI/gaIVgd50oe3v7Ji8PTjklg4bfbmzuugsOPth9/OGHqQs6PfUURd99tzHotK5Pn6w6BmWlAQPcoNN33zWQXpMBPv3UnT7gAN/fPp1Bp6VL3elaX7t19eljg4bBAJzXvffC66/b0VvbtbPXOtdck/LCPXl5Nrm4oYEw06ZDB6oKc2zqCZC3zJ9RrJPiiy/c6R494KmnEn5LBZ38Vd8mfA44BjgE+MMY8zrwouM44+p5jWSpiBejxcX2gm7WLLv3TZqUmXd51L0uZk22kDjYoFMweAT24BRN0Olf/3Knzz4bBg70v21JFHd2m/fqNcqgU+/evfky7mrl4dXU1FBSUsLixYtZClwLtGvd2hZ1rsfate70I4+k9hrN+9X0yivw88/Rva5///4MGjQIk0D9kKiCTt2722L4a9bYx++/b3++/94OLVlSQpehQ+HZZ93XHHpo3G2KSs+eNugE8MEHNuAVRXZdUNK/27yZtV261Knx4r3GefJJJaI0OkOG2Cy84Ci5X3xh/+h+F7epbflyuOYamntuApSnOqLZFHmLEXuLFGeqjz92p6PMxI5FpmQ61dMLzioqgqefhieesNctjzziPnfMMXDZZfYAvcUWqhJdjw0dWtlR/YD8FSm+cxYL78AeZ53lS5RIQSd/RdyEjuOcbYy5CDgKOA34O3C+MWYqtqudsp0akXpP0nfZxQadwF4xZVrQ6dtvbVePoBgynTIi6yZNmnymU20VFfU3yHHc0b7AjviVZXzJdPJ2K0qxnJwcDjzwQF4JFIq8G+yF2LXXRv0ewYHR0uHFF2Nbvl+/fgxJIGIxebI7/eefto5XkDGG/fffn7333tt+p9fuCjk6kFK/YAHdvQEnSH79mEGDQodX/PLLmIJOSb8o8u4DYW5yeINOyY5DSJp062aL3k+ZYqO7paWh2U/JcM89sHIlRZ4PdXlNTXLXKfYcuKDAHjSnTQsNOGaa996z2Ttgs7GTMHJs1mQ6gZtGVF1ti6tPmwbbb2+/t42J6SZ1U1Xda0uqZ68gF8ivMtQsLiOn/SZie+XQAAAgAElEQVTpblYoxwkNOvlU6V1BJ3/Vuwkdx1kPvAq8aowpwRYUPwV7gxngDmPMY8DbgWUlS9V7Mdqnj61ZADbodMYZKWtXVG6+OfRxDH0KlOlkNblMpx13rDtv3rzQIuO1rVnjbqjmzbPyZCXu4vHe33XevLR2sz3uuOM2Bp0au/HjxzM+eAGRoClTQhP1AG6//XZ++ukndvjnP21dM+8ZfTglJbYLQjJqOXldd51t8Btv2McxZhck/bvNG3QKUy9FQacmYuhQ+zkFG6RNZtBpwQJ44AEAmnsOousUdEq+Zs1sVnOw+87FF9ts0Ezz2282AzvorLOiSAeKXVYFnYJyc20G2Pvv25sm2TACYYbI23kHlo/+nk2xB9PKO56mcN/tbQr5scdmRpbY9OluWnmrVjZQ7AMFnfwV9SZ0HGcBcCdwpzGmHzb76XjgReBhoG1SWigp0WDQKSjaviGp5C1w3q5dTHd2mnLQqUlnOvXpY7sIeU8c586tP+gU99lO5oi7e12zZjaYO3s21NTYUf969/a9fdE47LDDeLN/f34IZp0ddFC92ZcVFfDgg3Y6P99m1KfShx+6NYcHDw4toRSO4zi8++67TJ06Nelt27BhA0888QSPPvooLFkC69bZ79BwxacGDbJd3Zo3T3q7yM+HRx91g04//2zbFGVBrqReFJWWhtZ0UdCp6Tr4YLjvPjv9yCMwYoSd9/jj/gflb7jB7p9Akec4VV5d7e96JLzbb4c99rAZFR98YL+TUjJufRiOAytW2MLX3nknn2y/x8Geo/z730lZvff7NZU3LCHG7nW1desGF2kMrFjlNctlGQUbg04V979A4f2BNOqVK+2IxunmzXLaZx/fIkQKOvkrrk3oOM54YLwx5jLgMGwGlGSxqINOEybYi85oRvpKBcexmRdBM2fG9M3QlINOTTrTyRgYNcp22XnnHTvPW6clnEYQdErob967t1vP6bff0hZ0MsCxc+dybHDG3XeHz1wLWLLEDTq1agV33pnsFoZau9YNOh1ySHT1hv/973/zwQcfMGHChITWPW6cvT4CWxs8WIpp6dKlPPzwwwA88cQToZljtS5iDdjv+59/DhkWPlKtKb/mA5jcXNueqiqbZRWImjb0Xt5fYdo0KCmpZx2xtKu8HLN8eei8hx7CPPss++yzD88//zz5+fkKOjUVe+0VWqh4wQJ47jk7xNRpp/m3nokT7fsGFHmG7FKmU4rsvjscd5wbCB85Mj1Bp1Wr4IgjbPD7hhvcTP+FC0NHVBw1KilZTpClmU4St7w8WEoztsHWffyVuzEEojEX58N1yRmlOCaruwCBc/nPi2FTf9r0nxVuLaHfeyY/Qa7r9V3pfGn29aKIVkJxO8dxqoD/Bn4ki9V7MVpSYoedXLzYdjGaPh223jql7YuorMw96rVsaX9ikBEBkDRp0plOQd7MpoaCTsE7iJC1Zztxd68D26UqGMGYONG3NsVs0iR7cQd2f+/Vq97F0zlyXe11Rjt6XW5uLocffjiHH354Quu+9173T7bnnm5NJ8dxGD16NDNmzKCmpoaVK1fW/0Y1NfaOZjqtWBHXy6qr3YEqk2L5cli+nFdeeYWhQ4dy0kknKejUVDRrZouKjxwZOn/sWH+DTldfbfdBgMGDab7vvhtrsKmmUwode6wbdHrnHbjlltSu33HsqIWlpfbxww+7QadffnGXGzDABsmSJGsKiYsv8vJgPu4fvZpi98kqYNmGui9KuaLAD7AWWOtPm7zDNFSX+fKW9aopb9zf50oWE6CBoJMxtn/sRx/Zxz//nDlBJ2+Wk+cufLSU6WQ1uUynIG+toiaQ6RR39zoIzWzy3lFNpenTQ9vRv3+D9QTSvY97g06pPkGP9LsbY7jnnns45ZRTWBMctU4S9uOPP9YJOqWiN6Kk0V132W5v06fDjBl23jifBnmuqrLd9z780D42Bu66iyJPpnl5dTWO4yQ0ymU6VDsO36xcyaps6h642242uy14x+b332HzzVO3/j/+sKmz3oDSX3/ZyPbs2e78IUNCozM+W9oDCKzqewPFPq/qV2BthPbP68zGSMCEQpibvF9TAqZuAqU7NGevKXm0q8qEAFPjtayyiuirEmcfBZ0EiCIA0adPaNDp+ONT0q4GeccjjyPolFFZNymWrkyndAcBQsQbdNpss+S0J8kSCjR6i0ePGmXT/FM9XHftAuIHHdTgS7Ix08kv9f3uw4YNY9myZawL1InxcpzQwWnHjh3LnnvuGfa5SK+Jd37IcytW2BHCAo+d334Lu+/Vfq+VK+3LwKFFC1uCLOZ1e1VU4Lz4optVYIy9ACwq4n//+x9nB4r3/hYIxirTqQnp0cOeG61aBW3a2M/qxIk2OJCImho44AD46it33hlnwI47kg/kGcMGx6EaqHIcCrIo6OQ4DmdMmcJLixaluymx82Y3LVkSmgGdCrXrNE2fbv/fZpvQ55J5Y2hw4Ae4D7gvGauK1H7PQLWnzQfmJ2HdEqoH8BAcVw0tdI8qqYZvk4s/JdAzk4JOAkQZdAr66aektydqynSKW7oynTIq0OcNOv36q71KnzsXttqqbt2yRpDplFD3um23tXnWwcqKu+xiR4Opr/i637wDGWy7bVQFktK9j2dq0AmgoKCAgijSDVu2bEnbtmkYK6R9eztqVKArETvsYO/mjx4dWkS3Fm8sdMMG20M8IXfcETpKao8e0LMnAHvvvffG2b/++ivV1e53nDEZ8B0nqdGqlR2K/fffbcBol13o3a6dPXe6557Yo4+jR4cGnIqKQgIeRTk5rA5kCW03bhw5WRR0qnYcZqb6y1BEElKTC6tap7sVjVtNUYbUS04SBZ0EiDHo9PPP9m6ejyc5q1fb2iMx3xwc1R44004vHQrPxvZy7zldU7s4yIRMp7R3r+vdG1q3tqkRc+a4FwadO9sRMC64wNZHgEYRdEqoe11BAZx0Erz4on08Y4YNzrVtC2eeaS+sks0bdHrjjagu5NIddPKuM9OCTlnhjDPcoBPAd9/BJpvYquxvvhm2/1rtmiMJH64eeST0saeOWPfu3WnevDnr1q1j0aJFvPXWOxCoeZGfD59+msB6U2T58uWUlpayevXqtLajQ4cOXHnllWyeyi5Lfurf3wadAKZOZVOAb7+1x5Nrr63vlXXV/j59882QG2utcnM3Bp3+zOIAzk7FxXTM8JOv9evXUxU8SVqxws3CKS72bWj2Bi1dSvXkycwFFgMbK78EB1vwys9PasXjmuq6q2xYM2BnILa6q5G0bm3vR0jyzZnjlvHs3DmQ9L5iBXzzjZ1ZVGRHKmnRwn4wpk+358uR6kCWlNh6nO3a1XvzKKKyMnse4FVUZG9I+Zha7DihPZsPPti3t45oq0aeGq2gkwBRBJ26d7dfEqtX2y+TefOgUydf1u04cOCB8P338bz6iMAPMDrwE6cMP+/xnQqJYy9Y//nPuhcEc+fCyy/bTJ65c+0O0giCTglntz33nK1IfcEFbsbT8uW2YvUOO9iT8X794Oij7VF6zBjo2hW23DLxxi9bZs9+wH5wttsuqpelO/CSKZlOad/X4nX66fDss+4JbtAHH8CTT8Jll9V5SW6uTVSsqbHHlw0bQr/vojJrll13hw6hGbUQcuzLzc2lV69ejB8/HoATTzxy43OVlVH1ABWPmTNnMmLEiHQ3Iz4XXgjvvmsviry++CK2oNOXX9ofsNmlf/4ZmpULXNCxI/83cybZXHZ21xYt+LJPH4obqMuXTtdffz233XZbupvRKBQXb0eXLpc3uNy6dWtp3rw44vPFxbZsVdcYS6c1a9aMoUOH0k4VyGPy3A9wVmDAzAPOgOdODjyxbp0d1OW44+wfJSiYpDB2rK2BFklurg1QdesWW4Oeecaetwc98giceKK9GeWj8nJovrOdblYIH1zj69s3SQo6CRDFxWhOjv0iCaYG/fSTb0GnVaviDTj5K00jwKeNCokHXHKJrRUUrobA4sXwv//BYYcp6AT2JOGcc6BLFxg+PLSr7emnh39NcbEdca5LlzhW6OEdnad376ijCOnOdMqUQuJZm+mUk2Oz2i66yF7Qe735pr3dvcUWdY5HzZq5tZUqK+MIOt14o3vhX9uwYSEPBw4cuDHoJIl5//33WbVqFa1SXS8uTjU1NXz66acsCdb2uftum+00bZqtfQf25sXjj0dXA6+yEm691X08YEBoSnZAV+DhnBzKw2S05OTmsseAAWzi80WYn3KNoVthYUZ3C6yqquKBBx5IdzMajbVrJzN58jm+vNcPP8T3um7duvH1119nzfdLbcYYiosjB+SSIc8TKdjgrSM+ZEj9L/T2kAmnutqOxBjp3DGSYB0zsMfpCy+M7fVR8v6ueYqW+EKbUYAoL0a9Qaeff4YEh/MO8l7Lt2xpR6WN2ltv2uwrgCOGxT2G6o47+jvCcTbwXoT9+Sece25q1ptxXRqLiuwdmccfhzvvtJk7Xocfbg9qY8a487I06ORbdtvgwfbnoYfgH/+of9m1a+1dqSeftOnX8frxR3d6552jflkmBZ1GjYKddkrdumfPDt+OrNOpkx2i/PHHbZZd0Hff2RGlmjeH8ePtSFKXXgqtWlGQ/xDl5bY+QkVF6I3YqAS7kYabv99+IbOGDx9OeXk5M2bMYN260F4H2dAFxBjDDjvswI477khO7Vp2KXL77bczZcoUKisruffee+nXr19a2hGL6upq7rnnHsZ6u39G4v3cxmLMmNBjT5QKCgo499xz2S7KjNBUy83N5YADDmCrrbZKd1Mi+umnn1gbqPlQWFjotnX1ajfrNqhnT//vos2YUSc9tm3nzpx++OEc/uijdZd/4w3Yd19/2+CDV199lUsvvTTdzQBg1qxZdIyj/msm6d+/P5999hktEjmfikHEoFNDiovtzaLa3dO9vDcTo+UNOvXoEfvro+T9XTM4GTOrKOgkQAxBpyBvbZUEeUdG7dnT9qSIysqV8FxgFL28PHjjb7bbuETF+zcvK4Onn05vG9KqVSu45hq46iqbxRC8Qx1U+wQvS4NOvme3DR3acNAJ4NVX4ZNPbGGA9u3jW5f3wss7ZHQD0t29zhvsWLnS1qtPhxTfHE2O88+3dwiOOspmIQatW2cDUvn58NJLADTLuQlsVR0ql66CTVrZ10yaZIPIrVvDyJH2AHTMMTYt6qST7J3TSNkXxx0Hp5xSZ/Zmm23GE088Adhz6GBMtEcP+7GXhs2cOZMbb7wRgFu8I4RJXCorK3mkvou9DNC6dWt+/PHHjA08feW5Q3bsscfyojcQXV5uA97BDOlp0+C66+Bf//KnntIHH8Chh9rpwkJbHK5LF9vN8scf656TtGljz10y5qTKdckll1BcXMx3tevwRLBgwQJKEh79IdS4ceM2ji6a7caNG8dLL73E+eefn5L1xR10Anj4YXtMzcmxx9tBg+x5YDBb+IEH7EGzUyd78RdNSvK0ae50YECPZPDWLVOmkz+0GQXInKBTTIlKEya40716ZUjaTPbYemvbK2V+moaczc0Fz8BPmSF4YFy50u12V1vnzgo6BW21ld0ec+faxy+/7BZhr6ViyUoeu2Aqc7vEEXRyHPh4CDDIPh57FEyK7qWTPMul4yti331tSbw//0z9uoO6dauTnJO9Bg602UZDh9qiTUEjR4YUES2oKd84XdFvT/jnSaF1IMAGkdasgcmT7ePbb4fXXrNfjOFsv32DzSt3V+tnTdNG79hjj+Wmm27CcZx0NyUunTt3Zq9a9UvWfPcdLcLt+P3720BCTo694VFcbC+kvH2Gdt45qs9bbZWVlbz77rtu4esMtnLlSnr27EluhqYRVHuuOvepfUwrKrLZu3vs4c677TZbWPyooxJbsePATTe5j887LzRlsnt3ezAPHsS7doX778/IgBPYTMqzzz6bs88+O6rlS0tLGTRokO/tuOWWW3jwwQepSHU/d59s2LBhY9tHjhyZHUEncM+XzzrL/l97f//iC/v/Sy/BkUfa43vtLK7//tfub1On2nqLQSnKdFLQyR/ajAJEeTG63Xb2qq2iwqYWL1sWd3c2r7hL5XjryaRqBJFGJD/fxg5Hj05tTSewNwL33deeK2Wc3FxbkPDll+Guu2wXns8/t5/75s3taFpZegTyvXi8MXY4+UsusRWTTzjBbr/27UMzUYDruI17R+wZ74oAT/eUF+J7l3QEnYqK4I8/bFyjJg1Vf42x165Z+pENb/Bg+/lau9beDCkrq1PsuxnuhUXlqvK6AScIWyuHmTPtTzhhgqm1KegUn+22246XXnqJt956iw1xXdmkhzGGPffck6uuuqpO18SvR45k4NVXh3YHARjXQAXk4cNtACNOixcv5plnnuGvv/6K+z2Sqbq6mueee44NGzbgOE5W/L3rBJ3A1tvaf3/47DN33ksvJR50+uADNwBZWGizsL3atoUnnrA17Y4/3taGyODaWJnihhtu4IYbbkh3M+I2d+5cugRqY3766ad08qmubn1ycnLYfvuhwGNATnxBp9rqO/EfOdIOWHPJJba76Lhx9vj+n/+EXz6JdesUdPKfNqMAUQad8vNtRlEw2DNxYlQn4Q2JK9NpwQK43DMKRkMF6ySs9u1jr+HXpGyxhT2BTPQkMkN49/NFi+qOOhuX7ifBqBPtSW+wlvIVb9k7teXrAKginyc5z4eVJSZdmXV5eYFhhsU/7drZn2HD7ElqLQW4UdUKP/pdX3SRgk5JdvLJJ3PyySc3vGCWqGrb1mYwTZ8OfftGHkLca7PNwgdIY9C+fXuGDx+e0HskW9++fbn00ktZt25duptSr7y8PC644AJ6RMqoeP11e7MlGHh65x07HPPdd9tuv9tuG/2F8bvv2td7L7D//nc7xHxtZ5xhf6TJ6Ny5M3379uWHH37AcRzm1R5VNUnmzn0SOAQ4zJ+gkzH2ZCzcTR+A55+3WfQnnFD/+/Trl9Rgq4JO/tNmFCCGbje9e7tBp99/T1/QKZimGbTrrgm3Q6Sx8+7nY8faG7X+qH3g3xv4POySPYoXcH6/H+CQQ2z3kvpUrIc33oRfPF1pTzjRnmzEaPvtNXx9o3TnnbbGibcg6X330ezhEggkLFXi+eDvvDPcc4/NJJk1y/506mRHGHv7bdtV9Jln3LS0M8+0aZknnRTVCa73GlpBJwFsF5C//oJrrw2txdOnj72BtnChvarZbjv72UxRgeB0OuecczjrrLNCurBlImMMefVdcW66qa231LevO9jFp5+6N0KLimy2RqThkTdssMVjfvihzqiYFBbC1Vcn/ktIo3HjjTdy3HHHUe69u5ES7+Jb0Ans53r8eNhmm9BSKWCD82+/Hf51Rx9tA/lTp8Jll8W9esdxmDhxYr3ZoN7SIxUV8OGHsa2joKCA3XffPeWjDWYyBZ0EiCHo1KuXOz1xoi/rjjnotHy5vUAIGjYspsLCIk1VIDM7ra5eewPnlD4DJz0F5zQwfPLt98Ev17mPd98dXr4bMrMEiKTDppvai73XX7dFSzfZBM45h4LXW2wMOlVceAU8fabtHvvGG7ag3f772yfLy+3FnTFw/fV23hNPwLff2m4sMY7+pUwnCatFCxsgnTPH9rd98klbVBdsgNOYJtdFKicnJ22jJfru5pvhsMNsPSav8nJ48EF3pBbHsTUlpkyxqcYPPmi7Cm++ed33vOaa8FlO0mQdeuihLF26lLKysqSv65dffuHQYDF73gV6M3Omrf3ti1tusTceW7YMHShm5kwbpA9nq61sfbMNG2y6fhyNqa6uZuTIkXz99ddRv2bhQltGMlY77bQT48ePJz+aAulNgIJOAoTWOok66PT7776sO+aaTh995A4rsO22tg+wiDSoVy97I33EiBTXF5o1ExYtYi/GcCaBrlAvvthw0Ml7t6ugwNbZytCis5JGublw8sn2JyDkmHbsyXDrITa4VHsIw3CRIWNCCwTHQEEniai4GN57r+78xhJ4acoOOcRmaNxyC9x3X+gB9q237ONly2yAPNwFdXAwDoB//9uO/BBHRq80fs2bN6d58+ZJX09JSQnt27dn8eLFwFLgMqZPTyjBKHqRio7edVcKVu6fX375hREjRnBCQ10FmwgFnQSIM9Pp99/tXZsE787FnOnkPWn7298SWrdIU3PFFfYnpRY1hwPOCc2OHDvW3vWPlH7155+ho2QuWWJHehKJgveYVlGBHVI8BRR0EmmiWra0tZwuvNDu/AMG2KyNlSvD1p0La7/9bDdMkTTLzc3lhBNO4KGHHkp3U5Jmm222Ycsttwz73KpV8M03drply9ABJBuyYMECfgl0+T/11FP5+9//HtXrbrjhBi731ituZBR0EiCGoFOXLjZNfM0aGy1avBg6dEho3TEFnRwndKSQjamfIpKxOnSwXQlGjrSV64OZiq+/HrlmxSOPuNOHHKKAk8Qk6uxdnynoJNLEdetm/z/zTPi//6v7fHGxrWXTsaOt9zVwoB2JbvHi0OOeSJrddtttOE5nHn7YZud16NBwfe+4rVhhs9vXrnXnBWulOY6tIbzzzr6sKicnh8GDBzN48OCIy/z4o109QM+esdV0Wrx4MV26dKGiooKqqipWRjOIBFBRUdHwQllMQScBYgg65eTYg2WwYOKMGakNOk2dajMewNbu0JBQItmhuNhmJtbU2CGeAZ59Fq66qm625A8/2FoXQaeckrp2SqNQJ9MpRRR0EhHA1mTKybHF4rfZxp4rl5TY2oS1i5MffXR62ihSjxYtWnD66Vfy8MP28RZb+FjTKZxnn7UXhT//bO8c7bOP7VWzcKHNAkxhV+RERq9r3749t956K8OHD6cqUlfBJkhBJwFCT9BLS+2+HdHs57D9e4HzesBmia17wQJ3usGaTt5icwMHqhaCSLYZNszW1Vm/3gaR8/Pt6CXe0X0ef9ytiXHggXDccelpq2StTMh0SkHZDRHJVPn5MHx4ulshkhBvwMW30esiyc2F9u3Bm4HUu3fk0R+TKJGgE8CVV17JRRddxPr166N+TWHtmpONjIJOAoTWVl22DL74or6ld3Qn/RnADrAXCQ2OLOkNOu21l38rF5HUaNUKDj/cdicA29Vu//1ttHvaNFtA0js4wE03NblRnSRxynQSERFJTEqDThkk0aAT2CBSYw8kxUJBJwFs0lBJSWjWUaqdeGID15bLlsE777iPFXQSyU7/+hf8+qsdNhpsLYvtt6+7XPv2sNtuqW2bNAreTKdLLrE9XVJh1Sp3WkEnERHJZgo6xR90klDajALYbgAzZsD330cxlPro0XDvPXb6oMG+nM23bg277NLAQnfc4Z7Rb7ONhpMVyVZbbw2TJ8PXX9s0am/hSK/DDrPp1iIx8tadX73a/qRa69apX6eIiIhfFHRS0Mkv2oyyUVERDBoUxYLrgXsD/e+cPNgvRbeQ33rLnb7tNl2MimS7gQNt/aZTT637XKdOqUtPkUbn1FPhtdfSl727ww520EUREZFspaCTgk5+0WaU2G2xhTs9f35q1rl6Ncyebafz8mxNGBHJfoceWnfe8uU2VUUDBUicdtrJHjLKylK/bmNgs81UikxERLKbgk4KOvlFm1Fi5w06peo2crD2C9iuOfn5qVmviCRX27ahj1u3hjZt0tMWaVTy8+0o5SIiIhI7BZ0UdPJLRtxGNsZ0NsZ8YYyZbIz53Rjzj8D8TYwxnxhjpgX+b+t5zT+NMdONMX8YYwZHfnfx3aabuntgWRkccIAtyHTZZXYkqmT4/Xd3OlzBYRHJXnfd5U7fcUf62iEiIiIiQNMNOnkvZ1XNxR+ZErvbAFzhOM5PxpiWwI/GmE+A04HPHMe5wxhzLXAtcI0xZnvgBKAXsAXwqTFma8dxkhTxkBA5ObD55vDXX/bxZ5/Z/3/+2RaFOuII/9c5aZI73auX/+8vIulzySWwYoU9sp91VrpbIyIiItLkNdWgkzKd/JcRmU6O4yxwHOenwPRqYDLQETgCeCGw2AvAsMD0EcDrjuNUOI4zE5gO9E9tq5u4jh3Dz//tN3/X8/rr0LMnPPSQO0+ZTiKNS7NmdnCAW25R11kRERGRDKCgk4JOfsm4zWiM6Qb0Ab4HOjiOswBsYMoY0z6wWEfgO8/L/grMC/d+5wLnAnTo0IHS0tKktDuV1qxZk/bfo9Muu9Dj++/rzF/0xRcsKy/Hyc9nyd57J1YI2HHY4/zzKVixImT2uLVrWdcI/o6Sepmw74hkG+03IrHTfiMSO+03maWqygD7BKZrKC39KuVtWLiwGRMmtAFSNzrHr7+2BkoAWLJkAaWlf6Rs3fHIhv0mo4JOxpgWwAjgUsdxVpnIQ7+Ee8IJt6DjOE8BTwH07dvXGTRokA8tTa/S0lLS/nvstRdMngy1PuAdPv+cDp9/bh+8+iqceGL861iwwHa58TruOPqffrqGBZK4ZMS+I5JltN+IxE77jUjstN9klpoad7q6Oiflf5v582HwYKisTOlqQ3TuXMKgQSXpa0AUsmG/yYjudQDGmHxswOkVx3H+G5i9yBhTEni+BFgcmP8X0Nnz8k7A/FS1VbC1V95/H+6/3/6E8/XXia1j8mR3um9fW7T8jTcUcBIREREREUminBz3sstxQoNQqfDll+kNOIGquvglIzKdjE1pehaY7DjOfZ6n3gNOA+4I/P+uZ/6rxpj7sIXEewLjUtdiAaC4GC69FNassSPX1TZnTmLv7w069e5dd2h1ERERERERSYq8PKiqstMbNkBBQerWvW6dO92jBwwcmLp1gx276pxzUrvOxiojgk7AQOAU4DdjzITAvOHYYNObxpizgDnAsQCO4/xujHkTmIQd+e5CjVyXRi1aQGEhrF8fOt/PoNO22yb2XiIiIiIiIhK1TAk6DR4MjzySunWLvzIi6OQ4zlgiVwfbP8JrbgNuS1qjJDa1A04Ac+fG/37l5fDYY2L+gEgAABTFSURBVO7j7baL/71EREREREQkJukcwa683J1u3jy16xZ/ZUxNJ2mEyspswfF586JbduRIWL3aPj7mGNt5OEhBJxERERERkZRJZ9DJm+mkoFN2U9BJ/HFbhKSzsWPhiCNsQClc9bmJE+Gee6CkBI46Cg45BJYsgdGj3WUGDLAdeUVERERERCQlFHQSP2RE9zppBC6+2BYUb9vWBoxKS93nfvzRBpROOw2ef94dBuH88+GJJ0LfZ8wYOype0CabwOefa8Q6ERERERGRFMqUoFNRUWrXLf5SppP4o2VLuP12uOqqyKHoF16A116z0zNn1g04BT33nDv9j3/YIuUiIiIiIiKSMpkSdFKmU3ZT0En8V1wc+bkHHrD/jx8feZmxY93pgw7yp00iIiIiIiISNRUSFz8o6CT+u/DCyM/98AMsXWq73AVdey2ceGLdZZs3h759/W+fiIiIiIiI1EuZTuIH1XQS/+2zj63NtGKF7VI3Zw5Mn25HqHMc2Gyz0OV33TV0pLqgPn1Cv+lEREREREQkJRR0Ej/oil6SY8897f+HHmr/v/lmuOmm8Mv27QuLFtWdv+uuSWmaiIiIiIiI1C9Tgk4qJJ7d1L1OUuOww8LP32QT6NoVunSp+5yCTiIiIiIiImmRKUEnZTplN2U6SWrssgs8/jh8+aX9xhozxmY3XXQRGKOgk4iIiIiISAZR0En8oKCTpM7f/25/AKqqbEHxkhL7uHPn0GU7dYJtt01t+0RERERERATQ6HXiD3Wvk/TIz3cDTgBt28IOO9jpVq3glVcgNzc9bRMREREREWnilOkkflCmk2QGY2DUKHj/fVt8vGvXdLdIRERERESkyVLQSfygoJNkjq5d4cIL090KERERERGRJi9dQaeamtDudYWFqVu3+E/d60REREREREQkRLqCTuvXu9OFhZCjqEVW059PREREREREREKkK+ikrnWNi4JOIiIiIiIiIhIiXUEnjVzXuCjoJCIiIiIiIiIhlOkkflDQSURERERERERCKOgkflDQSURERERERERCZELQqagodeuV5FDQSURERERERERCZELQSZlO2S+v4UVEREREREREpCnxBp2efx6+/TY1650zx51W0Cn7KegkIiIiIiIiIiG8QaexY+1Pqql7XfZT9zoRERERERERCbHffuluARxwQLpbIIlSppOIiIiIiIiIhDj6aPjmG5g4MT3r33pr2Hvv9Kxb/KOgk4iIiIiIiIiEMAYGDLA/IvFS9zoREREREREREfGdgk4iIiIiIiIiIuI7BZ1ERERERERERMR3CjqJiIiIiIiIiIjvFHQSERERERERERHfKegkIiIiIiIiIiK+U9BJRERERERERER8p6CTiIiIiIiIiIj4TkEnERERERERERHxnYJOIiIiIiIiIiLiOwWdRERERERERETEdwo6iYiIiIiIiIiI7xR0EhERERERERER3ynoJCIiIiIiIiIivlPQSUREREREREREfKegk4iIiIiIiIiI+E5BJxERERERERER8Z2CTiIiIiIiIiIi4jsFnURERERERERExHcKOomIiIiIiIiIiO8UdBIREREREREREd8p6CQiIiIiIiIiIr5T0ElERERERERERHynoJOIiIiIiIiIiPhOQScREREREREREfGdgk4iIiIiIiIiIuI7BZ1ERERERERERMR3CjqJiIiIiIiIiIjvFHQSERERERERERHfKegkIiIiIiIiIiK+U9BJRERERERERER8p6CTiIiIiIiIiIj4TkEnERERERERERHxnYJOIiIiIiIiIiLiu6wOOhljhhhj/jDGTDfGXJvu9oiIiIiIiIiIiJW1QSdjTC7wKHAwsD1wojFm+/S2SkREREREREREIIuDTkB/YLrjOH86jlMJvA4ckeY2iYiIiIiIiIgIkJfuBiSgIzDX8/gvYLfaCxljzgXODTxcY4z5IwVtS7ZNgaXpboRIFtK+IxI77TcisdN+IxI77Tciscuk/aZruJnZHHQyYeY5dWY4zlPAU8lvTuoYY35wHKdvutshkm2074jETvuNSOy034jETvuNSOyyYb/J5u51fwGdPY87AfPT1BYREREREREREfHI5qDTeKCnMWZLY0wBcALwXprbJCIiIiIiIiIiZHH3OsdxNhhjLgL+B+QCzzmO83uam5Uqjaq7oEgKad8RiZ32G5HYab8RiZ32G5HYZfx+YxynThkkERERERERERGRhGRz9zoREREREREREclQCjqJiIiIiIiIiIjvFHTKMsaYIcaYP4wx040x16a7PSKZyBjT2RjzhTFmsjHmd2PMPwLzNzHGfGKMmRb4v2262yqSaYwxucaYn40x7wcea78RqYcxpo0x5m1jzJTAcWeA9huR+hljLguco000xrxmjCnUfiNSlzHmOWPMYmPMRM+8iPuKMeafgVjBH8aYwelpdSgFnbKIMSYXeBQ4GNgeONEYs316WyWSkTYAVziOsx2wO3BhYF+5FvjMcZyewGeBxyIS6h/AZM9j7Tci9XsQ+MhxnG2BnbD7j/YbkQiMMR2BS4C+juP0xg4KdQLab0TC+Q8wpNa8sPtK4HrnBKBX4DWPBWIIaaWgU3bpD0x3HOdPx3EqgdeBI9LcJpGM4zjOAsdxfgpMr8ZeAHTE7i8vBBZ7ARiWnhaKZCZjTCfgEOAZz2ztNyIRGGNaAXsDzwI4jlPpOM4KtN+INCQPKDLG5AHNgflovxGpw3Gcr4CyWrMj7StHAK87jlPhOM5MYDo2hpBWCjpll47AXM/jvwLzRCQCY0w3oA/wPdDBcZwFYANTQPv0tUwkIz0AXA3UeOZpvxGJrDuwBHg+0C31GWNMMdpvRCJyHGcecA8wB1gArHQc52O034hEK9K+kpHxAgWdsosJM89JeStEsoQxpgUwArjUcZxV6W6PSCYzxhwKLHYc58d0t0Uki+QBuwCPO47TB1iLugSJ1CtQf+YIYEtgC6DYGPO39LZKpFHIyHiBgk7Z5S+gs+dxJ2wqqojUYozJxwacXnEc57+B2YuMMSWB50uAxelqn0gGGggcboyZhe2+vZ8x5mW034jU5y/gL8dxvg88fhsbhNJ+IxLZAcBMx3GWOI5TBfwX2APtNyLRirSvZGS8QEGn7DIe6GmM2dIYU4AtEvZemtskknGMMQZbX2Oy4zj3eZ56DzgtMH0a8G6q2yaSqRzH+afjOJ0cx+mGPb587jjO39B+IxKR4zgLgbnGmG0Cs/YHJqH9RqQ+c4DdjTHNA+ds+2Prb2q/EYlOpH3lPeAEY0wzY8yWQE9gXBraF8I4TtqzrSQGxpih2JobucBzjuPcluYmiWQcY8yewBjgN9zaNMOxdZ3eBLpgT3iOdRyndmE+kSbPGDMIuNJxnEONMe3QfiMSkTFmZ2zx/QLgT+AM7I1d7TciERhjbgaOx444/DNwNtAC7TciIYwxrwGDgE2BRcCNwDtE2FeMMdcBZ2L3rUsdx/kwDc0OoaCTiIiIiIiIiIj4Tt3rRERERERERETEdwo6iYiIiIiIiIiI7xR0EhERERERERER3ynoJCIiIiIiIiIivlPQSUREREREREREfKegk4iISBNljDndGON4ftYaY2YZY0YaY44zxuTUWr5bYLnTY1jHIGPMTbXfq7HybNNu6W5LOMaYYcaYy9O4/lnGmJfjeN1Nns/pLM/84GfybF8bGrruTrX2kwOStS4REZHGpkmcAIqIiEi9jgUGAEOB/wMqgNeAj40xRZ7lFgSW+yCG9x4E3IjOOTLFMCBtQScfDACOTPE6FwfWe2GK1ysiIpL18tLdABEREUm7CY7jTPc8fskY8xbwFnAXcDGA4zgVwHdpaJ+kgTGmWeBvnjEcx0n5589xnErgO2NMYarXLSIiku1011FERETqcBxnBPAucI4xpjmE715njOlnjPnEGLPMGLPOGPOnMeaxwHM3YbOcAKqC3ZM8r73ZGPOTMWalMWapMeZzY8zu3nYEuuc5xpjDjTGPBJZbYox52RjTptayecaYa4wxk4wx6wPLfWSM2dazzKbGmMeNMfOMMRXGmCnGmHMb2h7GmEJjzP3GmInGmDXGmIXGmFHe967ntfnGmFsDXcsqA//faozJ9ywT3LbnGWNuMcYsMMasCKyjU633ax74HZYZY1YHukPu0VDXR2PMf4DTgI61u6p5tvNRxpinjTFLgEWB53oYY14yxsw0xpQH/saPG2PahlnHPoHPw8pAd81fjDFn1dOmXGPMU8aYVcaY/RvaltEI/I2/N8ZMNsZ0CcwrNcaMNcYMMcZMCPwePxtjdgt8bm4PbPMyY8x/jDHFfrRFRESkqVOmk4iIiEQyGtsdqy/wVe0n/7+9ew/yqi7jOP5+uFggNQmJslJS0wSURRfLIoGk7AZoA2UbaZA5OtkgdMNyINbxUkkFykxq0mRX1KTbkOziDgQmDURGoQYqkoRmBgajxepCT3883xOHs7/f+e3lJwR+XjM7Z3/nfL/nPOf89o+dZ57vc8xsANACrAemA08Bw4DRachiYCjwSeB0YH/hFCcBC4AdwLHAucAaMzvV3f9UGHstsAyYCgwnKrD2E0mUzC0p3oVAK/BCYCwwBNhsZi8G7gb6AU3ANuC9wPWpqmdRybN4AfAi4EpimeFA4GKiAmaEuz9eMvd7wDnA1cBviKVac4BXpvvJ+xKwFjgfGAx8A/gRMC435tvEksgmYAPwrjSmliuA44G3AGelfcVKpkXAcuA84vkBNBDf0Szgnynuy4i/j7dnE83sbGAp8YwvAnYCrwVOrhRMWrq5JJ3jne5+TyfuoZRFL62WFOfp7r4rd/hVwHzgKuBp4m/ol+mnD/E3PDKNeQKY3dN4REREnu+UdBIREZFqtqftkCrHRwDHAbMLSaKbAdx9h5ntSPvWufu+/GR3/1/zZzPrDTQD9xFJqpmFa61x9xnp9xVmNhy4wMymu7ub2XhgCjDT3a/Lzft57veZRALkde7+YNrXmiqm5pnZ9cUYc7HuAYrxthDVQB8lkmcdmNkp6fjl7t6Ui38/cIWZfbXw7B5x96m5+ccD882swd0fS/c9Ffiiu1+Tht1pUY02gxLuvjVVMD1bskxtff57SfPWkEs6mtla4CHgLjN7o7v/wcyMSAxuBM5w9/+k4a1VnstxRLKnAXhHYXlnt5jZKCJhthH4kLv/uzBkEDDa3R9O43sR1XyvcPesOXiLmY0lknpKOomIiPSQlteJiIhINZa2XuX4g8Bu4EYzO9fMXtalk5u928xWmdkuYB/QDryaqGQqKjYv30RUH52QPr8nxXlTySXfB6wDtqUlVX3MrA+RPBoEvKZGvOekZVu7U7z/AgZUiTczNm2Lb2zLPo8r7K90nwAvT9vTiO/lJ4Vxt5fE0BU/K+4ws2PM7LK0FHEv8T3dlQ4Pz21PBhbnEk7VNKT5A6hTwol4zquJJNdZFRJOAA9kCadkc9q2FMZtBoamRJqIiIj0gJJOIiIiUk2WRPpbpYOp+ucM4DHgW8D21PNoSq0Tm9mbiOVZTxOVTW8jln39kQPLuvKeLHzOloVlYwcBT7r73pLLDiaSE+2FnyyBM6gk3knArcCfiUqj01K8/6gSb2Zg2haf4eOF45la95lVnT1RGPf3khi6otJ3/RViKd8PgQnAW4HJhbiyZ7eD2l5PLLu7pcayxK74AJHEurFatRqx5C7v2ZL9fYDedYpNRETkeUvL60RERKSaCUAb8PtqA9x9IzAlVQydSvQkus3MRrn7vSXnnkJUC0129/ZsZ1p2tbsbse4EBppZv5LE0y4iWVNcupfZUnL+RuAhd5+ei7UvHZNGRVkS6URga27/ibmYuiJLCg0melJlTqgwtjsqVbU1At939yuzHamfV97OtD2pE9doJpKL15hZm7tf261IDzaXqHZbbmbvd/e763BOERER6SFVOomIiEgHZjaZaDZ9Q5WlSgdx932pT9Bc4v+LkelQVqnTrzClP9EIPP82u/EcWEbWVSuIZWcXlIxpJvpQbXf3DRV+niqZ259IkuWdR+1qmNVp21jY/7G07dCgvYZ1xDP7cGF/8XM1z9Dxu6ilP1ERlveJwucHgL8QfbZqLktz9/nAZ4GFZvaZLsZTSTvRrH0F0GxmY+pwThEREekhVTqJiIjIG8zspcAxRNJnIpHEuJOoXKrIzCYCFxLNurcRb6C7hHiL3W/TsPvT9nNmthzY7+4biATQLOBmM/su0ctpLvBod27A3VeZ2VLgm6m31EqgL7Gc7lfu/mui2fdHiAbYC4jKpmOJRNQYdz+75BLNwAfTvGXAm9O9llZluft9ZrYEaErVYGuJt7XNBZZUeEtfrfvcYmY/JpqQ9yKq0MYDk9KQWv2U7icqwj5FvPmuzd031ZjTDEwzs01EA/HJHHhDYRaXm9ks4KfASjO7gVh6OBIY7O7zKtzLgtRQfaGZ9Xb3r9eIo5S7t5tZI/Emv+VmNsHdV9eaJyIiIs8dJZ1EREQk62nURiw/u4eozLnd3as1EYdoJL6XSKAMIZJNvwPOdPest88yot/TxcCXiWokc/cWM7uEqHaZAtwLfByY04P7aAQuBaYRCa09KZ7FED2ozGx0iuNSYinYbiL5tLTGuW8ielydD1yUzjuJCo23K5gGPJzmziF6YH0NuLzzt3aQC4lnPZtIFK4EPk086z015i4m+mddDbwEeAQYVmPODOJ7uyp9voN4I9/6/CB3/4WZnUn8PXwn7d4KLKx2Yne/LiWeFplZr9wb+brF3feZ2VTgB8AdZjbR3Vf15JwiIiLSfVb+v6SIiIiI/L8zsy8Qiaxh7r79cMdTb2bWBMwjqtfc3fcf4uv3Id402EokVVsP5fVFRESOVKp0EhERETmCpGWNpwAbieV0Y4DPA7cdjQmngnY6V51VN2Y2FPjrobqeiIjI0USVTiIiIiJHEDMbR1Q1jSB6Uj0K3ArMc/e2wxnbc8XMGoCG9PGZTvShque1+wKjcru21Gg6LyIiIomSTiIiIiIiIiIiUne9DncAIiIiIiIiIiJy9FHSSURERERERERE6k5JJxERERERERERqTslnUREREREREREpO6UdBIRERERERERkbr7L1kNEvJV1LrgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Indices to plot\n",
    "ind0 = 1000\n",
    "indf = 2000\n",
    "indices = np.arange(ind0, indf)\n",
    "\n",
    "# Gradient of topography \n",
    "# topo_gradient = np.gradient(dem_obs[indices])\n",
    "\n",
    "# Calculate SNOWPACK multiplicative factor\n",
    "# By how many timesteps have run\n",
    "# hy = 24*365 # Hours in a year\n",
    "# snowpack_factor = hy / len(ds['time'])\n",
    "\n",
    "# Have the same mean as observations\n",
    "# snowpack_factor = 1000 * obs_accumulation[indices].mean() / SNOWPACK_SMB[indices].mean()\n",
    "# M2_factor = 1000 * obs_accumulation[indices].mean() / M2_obs[indices].mean()\n",
    "# R2_factor = 1000 * obs_accumulation[indices].mean() / R2_obs[indices].mean()\n",
    "# MAR_factor = 1000 * obs_accumulation[indices].mean() / MAR_obs[indices].mean()\n",
    "# gradient_factor = 1000 * obs_accumulation[indices].mean() / topo_gradient.mean()\n",
    "\n",
    "# # Constant factor\n",
    "snowpack_factor = 1\n",
    "M2_factor = 1\n",
    "R2_factor = 1\n",
    "MAR_factor = 1\n",
    "gradient_factor = 1\n",
    "\n",
    "# Calculate distance along track (dat)\n",
    "dat = np.zeros(len(indices)); dat[:] = np.nan\n",
    "x0 = obs_X[ind0]; y0 = obs_Y[ind0]\n",
    "count = -1 # Add hock index counter \n",
    "\n",
    "for j in range(ind0, indf):\n",
    "    count = count + 1\n",
    "    dat[count] = np.sqrt(np.square(obs_X[j] - x0) + np.square(obs_Y[j] - y0)) / 1000\n",
    "\n",
    "# Plot \n",
    "fig, ax1 = plt.subplots(figsize=(20,7))\n",
    "plt.plot(dat, 1000*obs_accumulation[indices], 'r', label='Observations', linewidth=3)\n",
    "plt.plot(dat, snowpack_factor*SNOWPACK_SMB[indices], 'b', label='Alpine-3D', linewidth=3)\n",
    "plt.plot(dat, R2_factor * R2_obs[indices], 'k', label='RACMO2', linewidth=3)\n",
    "plt.plot(dat, M2_factor * M2_obs[indices], 'c', label='MERRA-2', linewidth=3)\n",
    "plt.plot(dat, MAR_factor * MAR_obs[indices], 'm', label='MAR', linewidth=3)\n",
    "plt.legend(fontsize=14)\n",
    "plt.grid()\n",
    "plt.ylabel(\"Annual SMB [mm.w.e.]\", fontsize=16)\n",
    "plt.xlabel(\"Distance along track [km]\", fontsize=16)\n",
    "plt.title(\"1980 Surface Mass Balance\", fontsize=16)\n",
    "plt.ylim([0, 1100])\n",
    "plt.savefig('SMB_Transect.pdf', format='pdf', dpi=100)\n",
    "\n",
    "# # Plot elevation\n",
    "# plt.figure(figsize=(15,5))\n",
    "# plt.plot(dat, gradient_factor * topo_gradient, label=\"Elevation\")\n",
    "# plt.xlabel(\"Distance along track [km]\")\n",
    "# plt.ylabel(\"Elevation [m]\")\n",
    "\n",
    "# Print mean along track\n",
    "print(\"Mean Observations\")\n",
    "print(\"    \" + str(1000 * obs_accumulation[indices].mean()))\n",
    "print()\n",
    "\n",
    "print(\"Mean Alpine-3D\")\n",
    "print(\"    \" + str(snowpack_factor*SNOWPACK_SMB[indices].mean()))\n",
    "print(\"    Alpine-3D Factor = \" + str(snowpack_factor))\n",
    "print()\n",
    "\n",
    "print(\"Mean MERRA-2\")\n",
    "print(\"    \" + str(M2_factor*M2_obs[indices].mean()))\n",
    "print(\"    MERRA-2 Factor = \" + str(M2_factor))\n",
    "print()\n",
    "\n",
    "print(\"Mean RACMO2\")\n",
    "print(\"    \" + str(R2_factor*np.mean(R2_obs[indices])))\n",
    "print(\"    RACMO2 Factor = \" + str(R2_factor))\n",
    "print()\n",
    "\n",
    "print(\"Mean MAR\")\n",
    "print(\"    \" + str(MAR_factor*np.mean(MAR_obs[indices])))\n",
    "print(\"    MAR Factor = \" + str(MAR_factor))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot of SNOWPACK and observed SMB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b2458e21080>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "space = 10\n",
    "plt.scatter(1000*obs_accumulation[indices[::space]], snowpack_factor*SNOWPACK_SMB[indices[::space]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalized observations and models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2b2454553b00>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load in 1980 SNOWPACK SWE\n",
    "x_snowpack = ds['easting']\n",
    "y_snowpack = ds['northing']\n",
    "swe = ds['swe'].isel(time=-1) - ds['swe'].isel(time=0)\n",
    "\n",
    "# Normalize data\n",
    "swe_norm = (swe - np.mean(swe)) / np.std(swe)\n",
    "obs_swe_norm = (obs_accumulation - np.nanmean(obs_accumulation)) / np.nanstd(obs_accumulation)\n",
    "\n",
    "plt.pcolormesh(x_snowpack, y_snowpack, swe_norm)\n",
    "plt.scatter(obs_X[indices], obs_Y[indices], c=obs_swe_norm[indices])\n",
    "plt.colorbar()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "alpine3d",
   "language": "python",
   "name": "alpine3d"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
